image processing thesis ideas

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What is Digital Image Processing?

Digital image processing is the process of using computer algorithms to perform image processing on digital images. Latest topics in digital image processing for research and thesis are based on these algorithms. Being a subcategory of digital signal processing, digital image processing is better and carries many advantages over analog image processing. It permits to apply multiple algorithms to the input data and does not cause the problems such as the build-up of noise and signal distortion while processing. As images are defined over two or more dimensions that make digital image processing “a model of multidimensional systems”. The history of digital image processing dates back to early 1920s when the first application of digital image processing came into news. Many students are going for this field for their m tech thesis as well as for Ph.D. thesis. There are various thesis topics in digital image processing for M.Tech, M.Phil and Ph.D. students. The list of thesis topics in image processing is listed here. Before going into topics in image processing , you should have some basic knowledge of image processing.

Latest research topics in image processing for research scholars:

The hybrid classification scheme for plant disease detection in image processing
The edge detection scheme in image processing using ant and bee colony optimization
To improve PNLM filtering scheme to denoise MRI images
The classification method for the brain tumor detection
The CNN approach for the lung cancer detection in image processing
The neural network method for the diabetic retinopathy detection
The copy-move forgery detection approach using textual feature extraction method
Design face spoof detection method based on eigen feature extraction and classification
The classification and segmentation method for the number plate detection
Find the link at the end to download the latest thesis and research topics in Digital Image Processing

Formation of Digital Images

Firstly, the image is captured by a camera using sunlight as the source of energy. For the acquisition of the image, a sensor array is used. These sensors sense the amount of light reflected by the object when light falls on that object. A continuous voltage signal is generated when the data is being sensed. The data collected is converted into a digital format to create digital images. For this process, sampling and quantization methods are applied. This will create a 2-dimensional array of numbers which will be a digital image.

Why is Image Processing Required?

Image Processing serves the following main purpose:
Visualization of the hidden objects in the image.
Enhancement of the image through sharpening and restoration.
Seek valuable information from the images.
Measuring different patterns of objects in the image.
Distinguishing different objects in the image.

Applications of Digital Image Processing

There are various applications of digital image processing which can also be a good topic for the thesis in image processing. Following are the main applications of image processing:
Image Processing is used to enhance the image quality through techniques like image sharpening and restoration. The images can be altered to achieve the desired results.
Digital Image Processing finds its application in the medical field for gamma-ray imaging, PET Scan, X-ray imaging, UV imaging.
It is used for transmission and encoding.
It is used in color processing in which processing of colored images is done using different color spaces.
Image Processing finds its application in machine learning for pattern recognition.

List of topics in image processing for thesis and research

There are various in digital image processing for thesis and research. Here is the list of latest thesis and research topics in digital image processing:
Image Acquisition
Image Enhancement
Image Restoration
Color Image Processing
Wavelets and Multi Resolution Processing
Compression
Morphological Processing
Segmentation
Representation and Description
Object recognition
Knowledge Base

1. Image Acquisition:

Image Acquisition is the first and important step of the digital image of processing . Its style is very simple just like being given an image which is already in digital form and it involves preprocessing such as scaling etc. It starts with the capturing of an image by the sensor (such as a monochrome or color TV camera) and digitized. In case, the output of the camera or sensor is not in digital form then an analog-to-digital converter (ADC) digitizes it. If the image is not properly acquired, then you will not be able to achieve tasks that you want to. Customized hardware is used for advanced image acquisition techniques and methods. 3D image acquisition is one such advanced method image acquisition method. Students can go for this method for their master’s thesis and research.

2. Image Enhancement:

Image enhancement is one of the easiest and the most important areas of digital image processing. The core idea behind image enhancement is to find out information that is obscured or to highlight specific features according to the requirements of an image. Such as changing brightness & contrast etc. Basically, it involves manipulation of an image to get the desired image than original for specific applications. Many algorithms have been designed for the purpose of image enhancement in image processing to change an image’s contrast, brightness, and various other such things. Image Enhancement aims to change the human perception of the images. Image Enhancement techniques are of two types: Spatial domain and Frequency domain.

3. Image Restoration:

Image restoration involves improving the appearance of an image. In comparison to image enhancement which is subjective, image restoration is completely objective which makes the sense that restoration techniques are based on probabilistic or mathematical models of image degradation. Image restoration removes any form of a blur, noise from images to produce a clean and original image. It can be a good choice for the M.Tech thesis on image processing. The image information lost during blurring is restored through a reversal process. This process is different from the image enhancement method. Deconvolution technique is used and is performed in the frequency domain. The main defects that degrade an image are restored here.

4. Color Image Processing:

Color image processing has been proved to be of great interest because of the significant increase in the use of digital images on the Internet. It includes color modeling and processing in a digital domain etc. There are various color models which are used to specify a color using a 3D coordinate system. These models are RGB Model, CMY Model, HSI Model, YIQ Model. The color image processing is done as humans can perceive thousands of colors. There are two areas of color image processing full-color processing and pseudo color processing. In full-color processing, the image is processed in full colors while in pseudo color processing the grayscale images are converted to colored images. It is an interesting topic in image processing.

Important Digital Image Processing Terminologies

Stereo Vision and Super Resolution
Multi-Spectral Remote Sensing and Imaging
Digital Photography and Imaging
Acoustic Imaging and Holographic Imaging
Computer Vision and Graphics
Image Manipulation and Retrieval
Quality Enrichment in Volumetric Imaging
Color Imaging and Bio-Medical Imaging
Pattern Recognition and Analysis
Imaging Software Tools, Technologies and Languages
Image Acquisition and Compression Techniques
Mathematical Morphological Image Segmentation

Image Processing Algorithms

In general, image processing techniques/methods are used to perform certain actions over the input images, and according to that, the desired information is extracted in it. For that, input is an image, and the result is an improved/expected image associated with their task. It is essential to find that the algorithms for image processing play a crucial role in current real-time applications. Various algorithms are used for various purposes as follows,

Digital Image Detection
Image Reconstruction
Image Restoration
Image Enhancement
Image Quality Estimation
Spectral Image Estimation
Image Data Compression

For the above image processing tasks, algorithms are customized for the number of training and testing samples and also can be used for real-time/online processing. Till now, filtering techniques are used for image processing and enhancement, and their main functions are as follows,

Brightness Correction
Contrast Enhancement
Resolution and Noise Level of Image
Contouring and Image Sharpening
Blurring, Edge Detection and Embossing

Some of the commonly used techniques for image processing can be classified into the following,

Medium Level Image Processing Techniques – Binarization and Compression
Higher Level Image Processing Techniques – Image Segmentation
Low-Level Image Processing Techniques – Noise Elimination and Color Contrast Enhancement
Recognition and Detection Image Processing Algorithms – Semantic Analysis

Next, let’s see about some of the traditional image processing algorithms for your information. Our research team will guide in handpicking apt solutions for research problems . If there is a need, we are also ready to design own hybrid algorithms and techniques for sorting out complicated model .

Types of Digital Image Processing Algorithms

Hough Transform Algorithm
Canny Edge Detector Algorithm
Scale-Invariant Feature Transform (SIFT) Algorithm
Generalized Hough Transform Algorithm
Speeded Up Robust Features (SURF) Algorithm
Marr–Hildreth Algorithm
Connected-component labeling algorithm: Identify and classify the disconnected areas
Histogram equalization algorithm: Enhance the contrast of image by utilizing the histogram
Adaptive histogram equalization algorithm: Perform slight alteration in contrast for the equalization of the histogram
Error Diffusion Algorithm
Ordered Dithering Algorithm
Floyd–Steinberg Dithering Algorithm
Riemersma Dithering Algorithm
Richardson–Lucy deconvolution algorithm : It is also known as a deblurring algorithm, which removes the misrepresentation of the image to recover the original image
Seam carving algorithm : Differentiate the edge based on the image background information and also known as content-aware image resizing algorithm
Region Growing Algorithm
GrowCut Algorithm
Watershed Transformation Algorithm
Random Walker Algorithm
Elser difference-map algorithm: It is a search based algorithm primarily used for X-Ray diffraction microscopy to solve the general constraint satisfaction problems
Blind deconvolution algorithm : It is similar to Richardson–Lucy deconvolution to reconstruct the sharp point of blur image. In other words, it’s the process of deblurring the image.

Nowadays, various industries are also utilizing digital image processing by developing customizing procedures to satisfy their requirements. It may be achieved either from scratch or hybrid algorithmic functions . As a result, it is clear that image processing is revolutionary developed in many information technology sectors and applications.

Research Digital Image Processing Project Topics

Digital Image Processing Techniques

In order to smooth the image, substitutes neighbor median / common value in the place of the actual pixel value. Whereas it is performed in the case of weak edge sharpness and blur image effect.
Eliminate the distortion in an image by scaling, wrapping, translation, and rotation process
Differentiate the in-depth image content to figure out the original hidden data or to convert the color image into a gray-scale image
Breaking up of image into multiple forms based on certain constraints. For instance: foreground, background
Enhance the image display through pixel-based threshold operation
Reduce the noise in an image by the average of diverse quality multiple images
Sharpening the image by improving the pixel value in the edge
Extract the specific feature for removal of noise in an image
Perform arithmetic operations (add, sub, divide and multiply) to identify the variation in between the images

Beyond this, this field will give you numerous Digital Image Processing Project Topics for current and upcoming scholars . Below, we have mentioned some research ideas that help you to classify analysis, represent and display the images or particular characteristics of an image.

Latest 11 Interesting Digital Image Processing Project Topics

Acoustic and Color Image Processing
Digital Video and Signal Processing
Multi-spectral and Laser Polarimetric Imaging
Image Processing and Sensing Techniques
Super-resolution Imaging and Applications
Passive and Active Remote Sensing
Time-Frequency Signal Processing and Analysis
3-D Surface Reconstruction using Remote Sensed Image
Digital Image based Steganalysis and Steganography
Radar Image Processing for Remote Sensing Applications
Adaptive Clustering Algorithms for Image processing

Moreover, if you want to know more about Digital Image Processing Project Topics for your research, then communicate with our team. We will give detailed information on current trends, future developments, and real-time challenges in the research grounds of Digital Image Processing.

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20+ Image Processing Projects Ideas in Python with Source Code

Image Processing Projects Ideas in Python with Source Code for Hands-on Practice to develop your computer vision skills as a Machine Learning Engineer.

Perhaps the great French military leader Napolean Bonaparte wasn't too far off when he said, “A picture is worth a thousand words.” Ignoring the poetic value, if just for a moment, the facts have since been established to prove this statement's literal meaning. Humans, the truly visual beings we are, respond to and process visual data better than any other data type. The human brain is said to process images 60,000 times faster than text. Further, 90 percent of information transmitted to the brain is visual. These stats alone are enough to serve the importance images have to humans.

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Therefore, the domain of image processing, which deals with enhancing images and extracting useful information from them, has been growing exponentially since its inception. This process has developed and augmented popular platforms and libraries like MATLAB, scikit-image, and OpenCV . The technology forerunners and the world-renowned conglomerates such as Google, Apple, Microsoft, and Amazon dabble in Image processing. Therefore, the hands-on experience working on image processing projects can be an invaluable skill.

Image processing projects for beginners, 1) grayscaling images, 2) image smoothing, 3) edge detection, 4) skew correction, 5) image compression using matlab, 6) image effect filters , 7) face detection, 8) image to text conversion using matlab, 9) watermarking, 10) image classification using matlab, 11) background subtraction, 12) instance segmentation, 13) pose recognition using matlab, 14) medical image segmentation, 15) image fusion, 16) sudoku solver, 17) bar-code detection, 18) automatically correcting images’ exposure, 19) quilting images and synthesising texture, 20) signature verifying system, what is image processing with example, what can be done with image processing, which is the best software for image processing, 20+ image processing projects ideas.

With the vast expectations the domain bears on its shoulders, getting started with Image Processing can unsurprisingly be a little intimidating. As if to make matters worse for a beginner, the myriad of high-level functions implemented can make it extremely hard to navigate. Since one of the best ways to get an intuitive understanding of the field can be to deconstruct and implement these commonly used functions yourself, the list of image processing projects ideas presented in this section seeks to do just that!

New Projects

This section has easy image processing projects ideas for novices in Image processing. You will find this section most helpful if you are a student looking for image processing projects for the final year.

Grayscaling is among the most commonly used preprocessing techniques as it allows for dimensionality reduction and reduces computational complexity. This process is almost indispensable even for more complex algorithms like Optical Character Recognition, around which companies like Microsoft have built and deployed entire products (i.e., Microsoft OCR).

The output image shown above has been grayscaled using the rgb2gray function from scikit-image. (Image used from Image Processing Kaggle)

There are plenty of readily available functions in OpenCV, MATLAB, and other popular image processing tools to implement a grayscaling algorithm. For this image processing project, you could import the color image of your choice using the Pillow library and then transform the array using NumPy . For this project, you are advised to use the Luminosity Method, which uses the formula 0.21*R+0.72*G+0.07*B. The results look similar to the Grayscale image in the figure with minor variations in contrast because of the difference in the formula used. Alternatively, you could attempt to implement other Grayscaling algorithms like the Lightness and the Average Method.

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Image smoothing ameliorates the effect of high-frequency spatial noise from an image. It is also an important step used even in advanced critical applications like medical image processing, making operations like derivative computation numerically stable.

For this beginner-level image processing project, you can implement Gaussian smoothing. To do so, you will need to create a 2-dimensional Gaussian kernel (possibly from one-dimensional kernels using the outer product) by employing the NumPy library and then convoluting it over the padded image of your choice. The above output has been obtained from the scikit-image with the Multi-dimensional Gaussian filter used for smoothing. Observe how the ‘sharpness' of the edges is lost after the smoothing operation in this image processing project. The smoothing process can also be performed on the RGB image. However, a grayscale image has been used here for simplicity.

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Edge detection helps segment images to allow for data extraction. An edge in an image is essentially a discontinuity (or a sharp change) in the pixel intensity values of an image. You must have witnessed edge detection at play in software like Kingsoft WPS or your own smartphone scanners and, therefore, should be familiar with its significance.

For this project, you can implement the Sobel operator for edge detection. For this, you can use OpenCV to read the image, NumPy to create the masks, perform the convolution operations, and combine the horizontal and vertical mask outputs to extract all the edges.

The above image demonstrates the results obtained by applying the Sobel filter to the smoothed image.

NOTE: On comparing this to the results obtained by applying the Sobel filter directly to the Grayscaled image (without smoothing) as shown below, you should be able to understand one of the reasons why smoothing is essential before edge detection.

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Skew correction is beneficial in applications like OCR . The pain of skew correction is entirely avoided by having artificial intelligence -enabled features built into applications like Kingsoft WPS.

You can try using OpenCV to read and grayscale the image to implement your skew correction program. To eliminate the skew, you will need to compute the bounding box containing the text and adjust its angle. An example of the results of the skew correction operation has been shown. You can try to replicate the results by using this Kaggle dataset ImageProcessing .

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Quoting Stephen Hawking, “A picture is worth a thousand words...and uses up a thousand times the memory.” Despite the advantages images have over text data, there is no denying the complexities that the extra bytes they eat up can bring. Optimization, therefore, becomes the only way out.

If words alone haven't made the case convincing enough, perhaps the mention of the startup, Deep Render, which is based on applying machine learning to image compression, raising £1.6 million in seed funding, should serve to emphasize the importance of this domain succinctly.

For this MATLAB Image Processing project, you can implement the discrete cosine transform approach to achieve image compression. It is based on the property that most of the critical information of an image can be described by just a few coefficients of the DCT. You can use the Image Processing Toolbox software for DCT computation. The input image is divided into 8-by-8 or 16-by-16 blocks, and the DCT coefficients computed, which have values close to zero, can be discarded without seriously degrading image quality. You can use the standard ‘cameraman.tif' image as input for this purpose.

Image Credit: Mathworks.in

Intermediate Image Processing Projects Ideas

In the previous section, we introduced simple image processing projects for beginners. We will now move ahead with projects on image processing that are slightly more difficult but equally interesting to attempt.

You must have come across several off-the-shelf software capable of cartooning and adding an artistic effect to your images. These features are enabled on popular social media platforms like Instagram and Snapchat. Producing images with effects of your liking is possible by using Neural Style Transfer.

To implement a model to achieve Neural Style Transfer, you need to choose a style image that will form the ‘effect' and a content image. (You can use this dataset: Tamil Neural Style Transfer Dataset for this image processing project.) The feature portion of a pre-trained VGG-19 can be used for this purpose, and the weighted addition of the style and content loss function is to be optimized during backpropagation.

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Seldom would you find a smartphone or a camera without face detection still in use. This feature has become so mainstream that most major smartphone manufacturers, like Apple and Samsung, wouldn't explicitly mention its presence in product specifications.

For this project, you could choose one of the following two ways to implement face detection .

Image Processing Projects using OpenCV Python

For this approach, you could use the pre-trained classifier files for the Haar classifier. While this is not particularly hard to implement, there is much to learn from precisely understanding how the classifier works.

Deep Learning Approach

While dlib's CNN-based face detector is slower than most other methods, its superior performance compensates for its longer execution time. To implement this, you can simply use the pre-trained model from //dlib.net/files/mmod_human_face_detector.dat.bz2.

Irrespective of the approach you choose to go about with for the face detection task, you could use this Image Processing Random Faces Dataset on Kaggle.

Image-to-text conversion or Optical Character Recognition has been the basis of many popular applications such as Microsoft Office Lens and a feature of others such as Google documents. The prevalence of OCR systems is only rising as the world becomes increasingly digitized. Therefore, this digital image processing project will involve familiarizing yourself with accomplishing image-to-text conversion using MATLAB!

While converting image to text by Optical Character Recognition can be pretty easy with other programming languages like Python (for instance, using pyTesseract), the MATLAB implementation of this project can seem slightly unfamiliar. But unfamiliarity is all there is to this otherwise simple application. You can simply use the Computer Vision Toolbox to perform Optical Character Recognition. Additionally, you can use the pre-trained language data files in the OCR Language Data support files from the OCR Engine page, Tesseract Open Source OCR Engine. Further, as an extension of this project, you could try training your own OCR model using the OCR Trainer application for a specific set of characters, such as handwritten characters, mathematical characters, and so on.

Image Processing Projects: Image to Text Conversion

Some publicly available datasets you could use for training on handwritten characters include Digits 0-9: MNIST , A-Z in CSV format , and Math symbols.

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Watermarking is a helpful way to protect proprietary images and data; it essentially signs your work the way painters do to their artwork. For digital watermarking to be effective, it should be imperceivable to the human eye and robust to withstand image manipulations like warping, rotation, etc.

Image Processing Projects Idea: Watermarking an Image

For this project, you can combine Discrete Cosine Transform and Discrete Wavelet Transform for watermarking. You can implement an effective machine learning algorithm for watermarking by changing the wavelet coefficients of select DWT sub-bands followed by the application of DCT transform. Operations like DCT can be accomplished in Python Data Science Tutorial using the scipy library.

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Image Classification finds wide digital applications (for instance, it is used by Airbnb for room type classification) and industrial settings such as assembly lines to find faults, etc.

Image Processing Projects Idea: Image Classification

One way to achieve image classification with MATLAB using the Computer Vision Toolbox function is by employing a visual bag of words. This involves extracting feature descriptors from the training data to train the classifier. The training essentially consists of converting the extracted features into an approximated feature histogram based on the likeness or closeness of the descriptors using clustering to arrive at the image's feature vector. This classifier is then used for prediction. To start this task, you could use this (adorable!) cat, dog, and panda classifier dataset .

Advanced Python Image Processing Projects with Source Code

It is time to level up your game in image processing. After working on the above mentioned projects, we suggest you try out the following digital image processing projects using Python .

Background subtraction is an important technique used in applications like video surveillance. As video analysis involves frame-wise processing, this technique naturally finds its place in the image processing domain. It essentially separates the elements in the foreground from the background by generating a mask.

To model the background, you will need to initialize a model to represent the background characteristics and update it to represent the changes such as lighting during different times of the day or the change in seasons. The Gaussian Mixture Model is one of the many algorithms you can use for this image processing project. (Alternatively, you can use the OpenCV library, which has some High-level APIs which will significantly simplify the task. However, it is advised to understand their working before using them.) The dataset made available on: http://pione.dinf.usherbrooke.ca/ can be used with due acknowledgments.

While object detection involves finding the location of an object and creating bounding boxes, instance segmentation goes a step beyond by identifying the exact pixels representing an instance of every class of object that the machine learning model has been trained. Instance segmentation finds its use in numerous state-of-the-art use cases like self-driving cars.

It is advised to use Mask RCNN for this image segmentation problem. You can use the pre-trained mask_rcnn_coco.h5 model and then provide an annotated dataset. The following miniature traffic dataset is annotated in COCO format and should aid transfer learning .

Source Code: Image Segmentation using Mask RCNN Data Science Project

Pose estimation finds use in augmented reality, robotics, and even gaming. The computer vision or deep learning-based company, Wrnch, is based on a product designed to estimate human pose and motion and reconstruct human shape digitally as two or three-dimensional characters.

Using the Open Pose algorithm, you can implement pose estimation with the Deep Learning Toolbox in MATLAB. The pre-trained model can be used from //ssd.mathworks.com/supportfiles/vision/data/human-pose-estimation.zip. (For this project, you can use the MPII Human Pose Dataset (human-pose.mpi-inf.mpg.de/).

Image processing in the medical field is a topic whose benefits and scopes need no introduction. Healthcare product giants like Siemens Healthineers and GE Healthcare have already headed into this domain by introducing AI-Rad Companion and AIRx (or Artificial Intelligence Prescription), respectively.

To accomplish the medical image segmentation task, you can consider implementing the famous U-Net architecture; a convolutional neural network developed to segment biomedical images using the Tensorflow API. While there have been many advancements and developments since U-Net, the popularity of this architecture and the possible availability of pre-trained models will perhaps help you get started.

Using the two chest x-rays datasets from Montgomery County and Shenzhen Hospital, you can attempt lung image segmentation: hncbc.nlm.nih.gov/LHC-downloads/dataset.html. Alternatively, you can use the masks for the Shenzhen Hospital dataset .

Source Code: Medical Image Segmentation

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IImage fusion combines or fuses multiple input images to obtain new or more precise knowledge. In medical image fusion, combining multiple images from single or multiple modalities reduces the redundancy and augments the usefulness and capabilities of medical images in diagnosis. Many companies (like ImFusion) provide expert services, and others develop in-house solutions for their image processing use cases, such as image fusion.

For your image fusion project, you can consider Multifocus Image Fusion.You can use the method described by Slavica SaviÄ‡'s paper titled “Multifocus Image Fusion Based on Empirical Mode Decomposition” for this. The following publicly available multi-focus image datasets can be used to build and evaluate the solution: github.com/sametaymaz/Multi-focus-Image-Fusion-Dataset.

Image-Processing Projects using Python with Source Code on GitHub

This section is particularly for those readers who want solved projects on image processing using Python. We have mentioned the GitHub repository for each project so that you can understand the implementation of the projects deeply.

During summer holidays in childhood, most of us would have at least once tried playing the exciting game- Sudoku. It is common to get stuck when playing the game for the first time. But, the most challenging part is to go back and realize where one went wrong?

Well, if you are into image processing, you can build a project to solve the sudoku.

The project idea is to build an intelligent sudoku solver that can pull out the sudoku game from an input image and solve it. And the first step in creating such an application is to apply a gaussian blur filter to the image. After that, use adaptive gaussian thresholding, invert the colors, dilate the image, and use a convolutional neural network to recognize the puzzle. The last step is then to use mathematical algorithms to solve the puzzle.

FUN FACT: Sudoku is an abbreviation for “suuji wa dokushin ni kagiru” (japanese), which means “the numbers must remain single.”

Source Code on GitHub: GitHub Repository:neeru1207/AI_Sudoku

This is one of the fun digital image processing projects you should try. It will introduce you to exciting and intriguing image processing techniques while guiding you on building a system that can detect bar codes from an image.

The idea behind this project is simple. One must make a computer locate that area in an image with maximum contours. You will first need to convert the image into grayscale to get started. After that, use image processing methods like gradient calculation, blur image, binary thresholding, morphology, etc., and finally find the area with the highest number of contours. Then, label the area as a bar code. Additionally, you can use OpenCV to decode the barcode. Source Code on GitHub: pyxploiter/Barcode-Detection-and-Decoding

Instagram is one of the top 6 social networks with more than a billion users, and we hope you are not surprised. It's the era of social media platforms, and photographs/digital images are one of the best ways to convey what's going on in your life. And, where there are images, there are filters to beautify them. In this project, you will build a system that can automatically correct the exposure of an input image. This project will help you understand image processing techniques like Histogram Equalisation, Bi-Histogram based Histogram Equalisation, Contrast Limited Adaptive Histogram Equalisation, Gamma Transformation, Adaptive Gamma Transformation, Weighted Adaptive Gamma Transformation, Improved Adaptive Gamma Transformation, and Adaptive non-linear Stretching.

Source Code on GitHub: GitHub - 07Agarg/Automatic-Exposure-Correction

While creating images on platforms like Canva, one often comes across images with great texture but do not have a high resolution. Well, through image processing techniques, you can easily create a solution for such images.

This project will create an application that will take a textured image as input and extend that texture to form a higher resolution image. Additionally, you will use the texture and overlap it over another image, referred to as image quilting. This project will use various image processing methods to pick the right texture and create the desired images. You will understand how different mathematical functions like root-mean-square are utilized over pixels for images.

Source Code on GitHub: GitHub - ani8897/Image-Quilting-and-Texture-Synthesis

Can you recall those childhood days when you'd request your siblings to sign your leave application on your parents' behalf by forging their signatures? Forging signatures sounds like a funny thing when you are a kid but not as an adult. Someone can withdraw money from your bank account without you knowing it by forging your signature. So, how do banks make sure it's you and nobody else? You guessed it right; they use image processing.

In this project, you evaluate the score difference between two images of signatures; one would be the original, and the other would be the test image. You will learn how to apply deep learning models like CNN, SigNet, etc., on processed images to build the signature verification application.

Source Code on GitHub: GitHub - DefUs3r/Automatic-Signature-Verification

Concluding with a quote from George Bernard Shaw, “The only way to learn something is to do something.” While (digital) image processing and machine learning were long established in his time, it doesn't make his advice any less applicable. Projects, short and fun as they are, are a great way to improve your skills in any domain. So, if you've made it up to here, make sure you don't leave without taking up an image processing project or two, and before you know it, you'll have the skills and the project portfolio to show for it!

FAQs on Image Processing Projects

Image processing is a method for applying operations on an image to enhance or extract relevant information. It's a form of signal processing in which the input is an image, and the output is either that image or its features. Example: Grayscaling is a popular image processing technique that reduces computational complexity while minimizing dimensionality.

Image processing has various applications such as Pattern Recognition , Video processing, Machine/Robot Vision, Image sharpening and restoration, Color processing, Microscopic Imaging, etc.

Here is a list of the best software for image processing:

Adobe Photoshop

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A list of completed theses and new thesis topics from the Computer Vision Group.

Are you about to start a BSc or MSc thesis? Please read our instructions for preparing and delivering your work.

Below we list possible thesis topics for Bachelor and Master students in the areas of Computer Vision, Machine Learning, Deep Learning and Pattern Recognition. The project descriptions leave plenty of room for your own ideas. If you would like to discuss a topic in detail, please contact the supervisor listed below and Prof. Paolo Favaro to schedule a meeting. Note that for MSc students in Computer Science it is required that the official advisor is a professor in CS.

AI deconvolution of light microscopy images

Level: master.

Background Light microscopy became an indispensable tool in life sciences research. Deconvolution is an important image processing step in improving the quality of microscopy images for removing out-of-focus light, higher resolution, and beter signal to noise ratio. Currently classical deconvolution methods, such as regularisation or blind deconvolution, are implemented in numerous commercial software packages and widely used in research. Recently AI deconvolution algorithms have been introduced and being currently actively developed, as they showed a high application potential.

Aim Adaptation of available AI algorithms for deconvolution of microscopy images. Validation of these methods against state-of-the -art commercially available deconvolution software.

Material and Methods Student will implement and further develop available AI deconvolution methods and acquire test microscopy images of different modalities. Performance of developed AI algorithms will be validated against available commercial deconvolution software.

Al algorithm development and implementation: 50%.
Data acquisition: 10%.
Comparison of performance: 40 %.

Requirements

Interest in imaging.
Solid knowledge of AI.
Good programming skills.

Supervisors Paolo Favaro, Guillaume Witz, Yury Belyaev.

Institutes Computer Vison Group, Digital Science Lab, Microscopy imaging Center.

Contact Yury Belyaev, Microscopy imaging Center, [email protected] , + 41 78 899 0110.

Instance segmentation of cryo-ET images

Level: bachelor/master.

In the 1600s, a pioneering Dutch scientist named Antonie van Leeuwenhoek embarked on a remarkable journey that would forever transform our understanding of the natural world. Armed with a simple yet ingenious invention, the light microscope, he delved into uncharted territory, peering through its lens to reveal the hidden wonders of microscopic structures. Fast forward to today, where cryo-electron tomography (cryo-ET) has emerged as a groundbreaking technique, allowing researchers to study proteins within their natural cellular environments. Proteins, functioning as vital nano-machines, play crucial roles in life and understanding their localization and interactions is key to both basic research and disease comprehension. However, cryo-ET images pose challenges due to inherent noise and a scarcity of annotated data for training deep learning models.

Credit: S. Albert et al./PNAS (CC BY 4.0)

To address these challenges, this project aims to develop a self-supervised pipeline utilizing diffusion models for instance segmentation in cryo-ET images. By leveraging the power of diffusion models, which iteratively diffuse information to capture underlying patterns, the pipeline aims to refine and accurately segment cryo-ET images. Self-supervised learning, which relies on unlabeled data, reduces the dependence on extensive manual annotations. Successful implementation of this pipeline could revolutionize the field of structural biology, facilitating the analysis of protein distribution and organization within cellular contexts. Moreover, it has the potential to alleviate the limitations posed by limited annotated data, enabling more efficient extraction of valuable information from cryo-ET images and advancing biomedical applications by enhancing our understanding of protein behavior.

Methods The segmentation pipeline for cryo-electron tomography (cryo-ET) images consists of two stages: training a diffusion model for image generation and training an instance segmentation U-Net using synthetic and real segmentation masks.

1. Diffusion Model Training: a. Data Collection: Collect and curate cryo-ET image datasets from the EMPIAR database (https://www.ebi.ac.uk/empiar/). b. Architecture Design: Select an appropriate architecture for the diffusion model. c. Model Evaluation: Cryo-ET experts will help assess image quality and fidelity through visual inspection and quantitative measures 2. Building the Segmentation dataset: a. Synthetic and real mask generation: Use the trained diffusion model to generate synthetic cryo-ET images. The diffusion process will be seeded from either a real or a synthetic segmentation mask. This will yield to pairs of cryo-ET images and segmentation masks. 3. Instance Segmentation U-Net Training: a. Architecture Design: Choose an appropriate instance segmentation U-Net architecture. b. Model Evaluation: Evaluate the trained U-Net using precision, recall, and F1 score metrics.

By combining the diffusion model for cryo-ET image generation and the instance segmentation U-Net, this pipeline provides an efficient and accurate approach to segment structures in cryo-ET images, facilitating further analysis and interpretation.

References 1. Kwon, Diana. "The secret lives of cells-as never seen before." Nature 598.7882 (2021): 558-560. 2. Moebel, Emmanuel, et al. "Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms." Nature methods 18.11 (2021): 1386-1394. 3. Rice, Gavin, et al. "TomoTwin: generalized 3D localization of macromolecules in cryo-electron tomograms with structural data mining." Nature Methods (2023): 1-10.

Contacts Prof. Thomas Lemmin Institute of Biochemistry and Molecular Medicine Bühlstrasse 28, 3012 Bern ( [email protected] )

Prof. Paolo Favaro Institute of Computer Science Neubrückstrasse 10 3012 Bern ( [email protected] )

Adding and removing multiple sclerosis lesions with to imaging with diffusion networks

Background multiple sclerosis lesions are the result of demyelination: they appear as dark spots on t1 weighted mri imaging and as bright spots on flair mri imaging. image analysis for ms patients requires both the accurate detection of new and enhancing lesions, and the assessment of atrophy via local thickness and/or volume changes in the cortex. detection of new and growing lesions is possible using deep learning, but made difficult by the relative lack of training data: meanwhile cortical morphometry can be affected by the presence of lesions, meaning that removing lesions prior to morphometry may be more robust. existing ‘lesion filling’ methods are rather crude, yielding unrealistic-appearing brains where the borders of the removed lesions are clearly visible., aim: denoising diffusion networks are the current gold standard in mri image generation [1]: we aim to leverage this technology to remove and add lesions to existing mri images. this will allow us to create realistic synthetic mri images for training and validating ms lesion segmentation algorithms, and for investigating the sensitivity of morphometry software to the presence of ms lesions at a variety of lesion load levels., materials and methods: a large, annotated, heterogeneous dataset of mri data from ms patients, as well as images of healthy controls without white matter lesions, will be available for developing the method. the student will work in a research group with a long track record in applying deep learning methods to neuroimaging data, as well as experience training denoising diffusion networks..

Nature of the Thesis:

Literature review: 10%

Replication of Blob Loss paper: 10%

Implementation of the sliding window metrics:10%

Training on MS lesion segmentation task: 30%

Extension to other datasets: 20%

Results analysis: 20%

Fig. Results of an existing lesion filling algorithm, showing inadequate performance

Requirements:

Interest/Experience with image processing

Python programming knowledge (Pytorch bonus)

Interest in neuroimaging

Supervisor(s):

PD. Dr. Richard McKinley

Institutes: Diagnostic and Interventional Neuroradiology

Center for Artificial Intelligence in Medicine (CAIM), University of Bern

References: [1] Brain Imaging Generation with Latent Diffusion Models , Pinaya et al, Accepted in the Deep Generative Models workshop @ MICCAI 2022 , https://arxiv.org/abs/2209.07162

Contact : PD Dr Richard McKinley, Support Centre for Advanced Neuroimaging ( [email protected] )

Improving metrics and loss functions for targets with imbalanced size: sliding window Dice coefficient and loss.

Background The Dice coefficient is the most commonly used metric for segmentation quality in medical imaging, and a differentiable version of the coefficient is often used as a loss function, in particular for small target classes such as multiple sclerosis lesions. Dice coefficient has the benefit that it is applicable in instances where the target class is in the minority (for example, in case of segmenting small lesions). However, if lesion sizes are mixed, the loss and metric is biased towards performance on large lesions, leading smaller lesions to be missed and harming overall lesion detection. A recently proposed loss function (blob loss[1]) aims to combat this by treating each connected component of a lesion mask separately, and claims improvements over Dice loss on lesion detection scores in a variety of tasks.

Aim: The aim of this thesisis twofold. First, to benchmark blob loss against a simple, potentially superior loss for instance detection: sliding window Dice loss, in which the Dice loss is calculated over a sliding window across the area/volume of the medical image. Second, we will investigate whether a sliding window Dice coefficient is better corellated with lesion-wise detection metrics than Dice coefficient and may serve as an alternative metric capturing both global and instance-wise detection.

Materials and Methods: A large, annotated, heterogeneous dataset of MRI data from MS patients will be available for benchmarking the method, as well as our existing codebases for MS lesion segmentation. Extension of the method to other diseases and datasets (such as covered in the blob loss paper) will make the method more plausible for publication. The student will work alongside clinicians and engineers carrying out research in multiple sclerosis lesion segmentation, in particular in the context of our running project supported by the CAIM grant.

Fig. An annotated MS lesion case, showing the variety of lesion sizes

References: [1] blob loss: instance imbalance aware loss functions for semantic segmentation, Kofler et al, https://arxiv.org/abs/2205.08209

Idempotent and partial skull-stripping in multispectral MRI imaging

Background Skull stripping (or brain extraction) refers to the masking of non-brain tissue from structural MRI imaging. Since 3D MRI sequences allow reconstruction of facial features, many data providers supply data only after skull-stripping, making this a vital tool in data sharing. Furthermore, skull-stripping is an important pre-processing step in many neuroimaging pipelines, even in the deep-learning era: while many methods could now operate on data with skull present, they have been trained only on skull-stripped data and therefore produce spurious results on data with the skull present.

High-quality skull-stripping algorithms based on deep learning are now widely available: the most prominent example is HD-BET [1]. A major downside of HD-BET is its behaviour on datasets to which skull-stripping has already been applied: in this case the algorithm falsely identifies brain tissue as skull and masks it. A skull-stripping algorithm F not exhibiting this behaviour would be idempotent: F(F(x)) = F(x) for any image x. Furthermore, legacy datasets from before the availability of high-quality skull-stripping algorithms may still contain images which have been inadequately skull-stripped: currently the only solution to improve the skull-stripping on this data is to go back to the original datasource or to manually correct the skull-stripping, which is time-consuming and prone to error.

Aim: In this project, the student will develop an idempotent skull-stripping network which can also handle partially skull-stripped inputs. In the best case, the network will operate well on a large subset of the data we work with (e.g. structural MRI, diffusion-weighted MRI, Perfusion-weighted MRI, susceptibility-weighted MRI, at a variety of field strengths) to maximize the future applicability of the network across the teams in our group.

Materials and Methods: Multiple datasets, both publicly available and internal (encompassing thousands of 3D volumes) will be available. Silver standard reference data for standard sequences at 1.5T and 3T can be generated using existing tools such as HD-BET: for other sequences and field strengths semi-supervised learning or methods improving robustness to domain shift may be employed. Robustness to partial skull-stripping may be induced by a combination of learning theory and model-based approaches.

Dataset curation: 10%

Idempotent skull-stripping model building: 30%

Modelling of partial skull-stripping:10%

Extension of model to handle partial skull: 30%

Results analysis: 10%

Fig. An example of failed skull-stripping requiring manual correction

References: [1] Isensee, F, Schell, M, Pflueger, I, et al. Automated brain extraction of multisequence MRI using artificial neural networks. Hum Brain Mapp . 2019; 40: 4952– 4964. https://doi.org/10.1002/hbm.24750

Automated leaf detection and leaf area estimation (for Arabidopsis thaliana)

Correlating plant phenotypes such as leaf area or number of leaves to the genotype (i.e. changes in DNA) is a common goal for plant breeders and molecular biologists. Such data can not only help to understand fundamental processes in nature, but also can help to improve ecotypes, e.g., to perform better under climate change, or reduce fertiliser input. However, collecting data for many plants is very time consuming and automated data acquisition is necessary.

The project aims at building a machine learning model to automatically detect plants in top-view images (see examples below), segment their leaves (see Fig C) and to estimate the leaf area. This information will then be used to determine the leaf area of different Arabidopsis ecotypes. The project will be carried out in collaboration with researchers of the Institute of Plant Sciences at the University of Bern. It will also involve the design and creation of a dataset of plant top-views with the corresponding annotation (provided by experts at the Institute of Plant Sciences).

Contact: Prof. Dr. Paolo Favaro ( [email protected] )

Master Projects at the ARTORG Center

The Gerontechnology and Rehabilitation group at the ARTORG Center for Biomedical Engineering is offering multiple MSc thesis projects to students, which are interested in working with real patient data, artificial intelligence and machine learning algorithms. The goal of these projects is to transfer the findings to the clinic in order to solve today’s healthcare problems and thus to improve the quality of life of patients. Assessment of Digital Biomarkers at Home by Radar. [PDF] Comparison of Radar, Seismograph and Ballistocardiography and to Monitor Sleep at Home. [PDF] Sentimental Analysis in Speech. [PDF] Contact: Dr. Stephan Gerber ( [email protected] )

Internship in Computational Imaging at Prophesee

A 6 month intership at Prophesee, Grenoble is offered to a talented Master Student.

The topic of the internship is working on burst imaging following the work of Sam Hasinoff , and exploring ways to improve it using event-based vision.

A compensation to cover the expenses of living in Grenoble is offered. Only students that have legal rights to work in France can apply.

Anyone interested can send an email with the CV to Daniele Perrone ( [email protected] ).

Using machine learning applied to wearables to predict mental health

This Master’s project lies at the intersection of psychiatry and computer science and aims to use machine learning techniques to improve health. Using sensors to detect sleep and waking behavior has as of yet unexplored potential to reveal insights into health. In this study, we make use of a watch-like device, called an actigraph, which tracks motion to quantify sleep behavior and waking activity. Participants in the study consist of healthy and depressed adolescents and wear actigraphs for a year during which time we query their mental health status monthly using online questionnaires. For this masters thesis we aim to make use of machine learning methods to predict mental health based on the data from the actigraph. The ability to predict mental health crises based on sleep and wake behavior would provide an opportunity for intervention, significantly impacting the lives of patients and their families. This Masters thesis is a collaboration between Professor Paolo Favaro at the Institute of Computer Science ( [email protected] ) and Dr Leila Tarokh at the Universitäre Psychiatrische Dienste (UPD) ( [email protected] ). We are looking for a highly motivated individual interested in bridging disciplines.

Bachelor or Master Projects at the ARTORG Center

The Gerontechnology and Rehabilitation group at the ARTORG Center for Biomedical Engineering is offering multiple BSc- and MSc thesis projects to students, which are interested in working with real patient data, artificial intelligence and machine learning algorithms. The goal of these projects is to transfer the findings to the clinic in order to solve today’s healthcare problems and thus to improve the quality of life of patients. Machine Learning Based Gait-Parameter Extraction by Using Simple Rangefinder Technology. [PDF] Detection of Motion in Video Recordings [PDF] Home-Monitoring of Elderly by Radar [PDF] Gait feature detection in Parkinson's Disease [PDF] Development of an arthroscopic training device using virtual reality [PDF] Contact: Dr. Stephan Gerber ( [email protected] ), Michael Single ( [email protected]. ch )

Dynamic Transformer

Level: bachelor.

Visual Transformers have obtained state of the art classification accuracies [ViT, DeiT, T2T, BoTNet]. Mixture of experts could be used to increase the capacity of a neural network by learning instance dependent execution pathways in a network [MoE]. In this research project we aim to push the transformers to their limit and combine their dynamic attention with MoEs, compared to Switch Transformer [Switch], we will use a much more efficient formulation of mixing [CondConv, DynamicConv] and we will use this idea in the attention part of the transformer, not the fully connected layer.

Input dependent attention kernel generation for better transformer layers.

Publication Opportunity: Dynamic Neural Networks Meets Computer Vision (a CVPR 2021 Workshop)

Extensions:

The same idea could be extended to other ViT/Transformer based models [DETR, SETR, LSTR, TrackFormer, BERT]

Quantized ViT

Visual Transformers have obtained state of the art classification accuracies [ViT, CLIP, DeiT], but the best ViT models are extremely compute heavy and running them even only for inference (not doing backpropagation) is expensive. Running transformers cheaply by quantization is not a new problem and it has been tackled before for BERT [BERT] in NLP [Q-BERT, Q8BERT, TernaryBERT, BinaryBERT]. In this project we will be trying to quantize pretrained ViT models.

Quantizing ViT models for faster inference and smaller models without losing accuracy

Publication Opportunity: Binary Networks for Computer Vision 2021 (a CVPR workshop)

Extensions:

Having a fast pipeline for image inference with ViT will allow us to dig deep into the attention of ViT and analyze it, we might be able to prune some attention heads or replace them with static patterns (like local convolution or dilated patterns), We might be even able to replace the transformer with performer and increase the throughput even more [Performer].
The same idea could be extended to other ViT based models [DETR, SETR, LSTR, TrackFormer, CPTR, BoTNet, T2TViT]
Learning Transferable Visual Models From Natural Language Supervision [CLIP]
Visual Transformers: Token-based Image Representation and Processing for Computer Vision [ViT]
DeiT: Data-efficient Image Transformers [DeiT]
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding [BERT]
Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT [Q-BERT]
Q8BERT: Quantized 8Bit BERT [Q8BERT]
TernaryBERT: Distillation-aware Ultra-low Bit BERT [TernaryBERT]
BinaryBERT: Pushing the Limit of BERT Quantization [BinaryBERT]
Rethinking Attention with Performers [Performer]
End-to-End Object Detection with Transformers [DETR]
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers [SETR]
End-to-end Lane Shape Prediction with Transformers [LSTR]
TrackFormer: Multi-Object Tracking with Transformers [TrackFormer]
CPTR: Full Transformer Network for Image Captioning [CPTR]
Bottleneck Transformers for Visual Recognition [BoTNet]
Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet [T2TViT]

Multimodal Contrastive Learning

Recently contrastive learning has gained a lot of attention for self-supervised image representation learning [SimCLR, MoCo]. Contrastive learning could be extended to multimodal data, like videos (images and audio) [CMC, CoCLR]. Most contrastive methods require large batch sizes (or large memory pools) which makes them expensive for training. In this project we are going to use non batch size dependent contrastive methods [SwAV, BYOL, SimSiam] to train multimodal representation extractors.

Our main goal is to compare the proposed method with the CMC baseline, so we will be working with STL10, ImageNet, UCF101, HMDB51, and NYU Depth-V2 datasets.

Inspired by the recent works on smaller datasets [ConVIRT, CPD], to accelerate the training speed, we could start with two pretrained single-modal models and finetune them with the proposed method.

Extending SwAV to multimodal datasets
Grasping a better understanding of the BYOL

Publication Opportunity: MULA 2021 (a CVPR workshop on Multimodal Learning and Applications)

Most knowledge distillation methods for contrastive learners also use large batch sizes (or memory pools) [CRD, SEED], the proposed method could be extended for knowledge distillation.
One could easily extend this idea to multiview learning, for example one could have two different networks working on the same input and train them with contrastive learning, this may lead to better models [DeiT] by cross-model inductive biases communications.
Self-supervised Co-training for Video Representation Learning [CoCLR]
Learning Spatiotemporal Features via Video and Text Pair Discrimination [CPD]
Audio-Visual Instance Discrimination with Cross-Modal Agreement [AVID-CMA]
Self-Supervised Learning by Cross-Modal Audio-Video Clustering [XDC]
Contrastive Multiview Coding [CPC]
Contrastive Learning of Medical Visual Representations from Paired Images and Text [ConVIRT]
A Simple Framework for Contrastive Learning of Visual Representations [SimCLR]
Momentum Contrast for Unsupervised Visual Representation Learning [MoCo]
Bootstrap your own latent: A new approach to self-supervised Learning [BYOL]
Exploring Simple Siamese Representation Learning [SimSiam]
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments [SwAV]
Contrastive Representation Distillation [CRD]
SEED: Self-supervised Distillation For Visual Representation [SEED]

Robustness of Neural Networks

Neural Networks have been found to achieve surprising performance in several tasks such as classification, detection and segmentation. However, they are also very sensitive to small (controlled) changes to the input. It has been shown that some changes to an image that are not visible to the naked eye may lead the network to output an incorrect label. This thesis will focus on studying recent progress in this area and aim to build a procedure for a trained network to self-assess its reliability in classification or one of the popular computer vision tasks.

Contact: Paolo Favaro

Masters projects at sitem center

The Personalised Medicine Research Group at the sitem Center for Translational Medicine and Biomedical Entrepreneurship is offering multiple MSc thesis projects to the biomed eng MSc students that may also be of interest to the computer science students. Automated quantification of cartilage quality for hip treatment decision support. PDF Automated quantification of massive rotator cuff tears from MRI. PDF Deep learning-based segmentation and fat fraction analysis of the shoulder muscles using quantitative MRI. PDF Unsupervised Domain Adaption for Cross-Modality Hip Joint Segmentation. PDF Contact: Dr. Kate Gerber

Internships/Master thesis @ Chronocam

3-6 months internships on event-based computer vision. Chronocam is a rapidly growing startup developing event-based technology, with more than 15 PhDs working on problems like tracking, detection, classification, SLAM, etc. Event-based computer vision has the potential to solve many long-standing problems in traditional computer vision, and this is a super exciting time as this potential is becoming more and more tangible in many real-world applications. For next year we are looking for motivated Master and PhD students with good software engineering skills (C++ and/or python), and preferable good computer vision and deep learning background. PhD internships will be more research focused and possibly lead to a publication. For each intern we offer a compensation to cover the expenses of living in Paris. List of some of the topics we want to explore:

Photo-realistic image synthesis and super-resolution from event-based data (PhD)
Self-supervised representation learning (PhD)
End-to-end Feature Learning for Event-based Data
Bio-inspired Filtering using Spiking Networks
On-the fly Compression of Event-based Streams for Low-Power IoT Cameras
Tracking of Multiple Objects with a Dual-Frequency Tracker
Event-based Autofocus
Stabilizing an Event-based Stream using an IMU
Crowd Monitoring for Low-power IoT Cameras
Road Extraction from an Event-based Camera Mounted in a Car for Autonomous Driving
Sign detection from an Event-based Camera Mounted in a Car for Autonomous Driving
High-frequency Eye Tracking

Email with attached CV to Daniele Perrone at [email protected] .

Contact: Daniele Perrone

Object Detection in 3D Point Clouds

Today we have many 3D scanning techniques that allow us to capture the shape and appearance of objects. It is easier than ever to scan real 3D objects and transform them into a digital model for further processing, such as modeling, rendering or animation. However, the output of a 3D scanner is often a raw point cloud with little to no annotations. The unstructured nature of the point cloud representation makes it difficult for processing, e.g. surface reconstruction. One application is the detection and segmentation of an object of interest. In this project, the student is challenged to design a system that takes a point cloud (a 3D scan) as input and outputs the names of objects contained in the scan. This output can then be used to eliminate outliers or points that belong to the background. The approach involves collecting a large dataset of 3D scans and training a neural network on it.

Contact: Adrian Wälchli

Shape Reconstruction from a Single RGB Image or Depth Map

A photograph accurately captures the world in a moment of time and from a specific perspective. Since it is a projection of the 3D space to a 2D image plane, the depth information is lost. Is it possible to restore it, given only a single photograph? In general, the answer is no. This problem is ill-posed, meaning that many different plausible depth maps exist, and there is no way of telling which one is the correct one. However, if we cover one of our eyes, we are still able to recognize objects and estimate how far away they are. This motivates the exploration of an approach where prior knowledge can be leveraged to reduce the ill-posedness of the problem. Such a prior could be learned by a deep neural network, trained with many images and depth maps.

CNN Based Deblurring on Mobile

Deblurring finds many applications in our everyday life. It is particularly useful when taking pictures on handheld devices (e.g. smartphones) where camera shake can degrade important details. Therefore, it is desired to have a good deblurring algorithm implemented directly in the device. In this project, the student will implement and optimize a state-of-the-art deblurring method based on a deep neural network for deployment on mobile phones (Android). The goal is to reduce the number of network weights in order to reduce the memory footprint while preserving the quality of the deblurred images. The result will be a camera app that automatically deblurs the pictures, giving the user a choice of keeping the original or the deblurred image.

Depth from Blur

If an object in front of the camera or the camera itself moves while the aperture is open, the region of motion becomes blurred because the incoming light is accumulated in different positions across the sensor. If there is camera motion, there is also parallax. Thus, a motion blurred image contains depth information. In this project, the student will tackle the problem of recovering a depth-map from a motion-blurred image. This includes the collection of a large dataset of blurred- and sharp images or videos using a pair or triplet of GoPro action cameras. Two cameras will be used in stereo to estimate the depth map, and the third captures the blurred frames. This data is then used to train a convolutional neural network that will predict the depth map from the blurry image.

Unsupervised Clustering Based on Pretext Tasks

The idea of this project is that we have two types of neural networks that work together: There is one network A that assigns images to k clusters and k (simple) networks of type B perform a self-supervised task on those clusters. The goal of all the networks is to make the k networks of type B perform well on the task. The assumption is that clustering in semantically similar groups will help the networks of type B to perform well. This could be done on the MNIST dataset with B being linear classifiers and the task being rotation prediction.

Adversarial Data-Augmentation

The student designs a data augmentation network that transforms training images in such a way that image realism is preserved (e.g. with a constrained spatial transformer network) and the transformed images are more difficult to classify (trained via adversarial loss against an image classifier). The model will be evaluated for different data settings (especially in the low data regime), for example on the MNIST and CIFAR datasets.

Unsupervised Learning of Lip-reading from Videos

People with sensory impairment (hearing, speech, vision) depend heavily on assistive technologies to communicate and navigate in everyday life. The mass production of media content today makes it impossible to manually translate everything into a common language for assistive technologies, e.g. captions or sign language. In this project, the student employs a neural network to learn a representation for lip-movement in videos in an unsupervised fashion, possibly with an encoder-decoder structure where the decoder reconstructs the audio signal. This requires collecting a large dataset of videos (e.g. from YouTube) of speakers or conversations where lip movement is visible. The outcome will be a neural network that learns an audio-visual representation of lip movement in videos, which can then be leveraged to generate captions for hearing impaired persons.

Learning to Generate Topographic Maps from Satellite Images

Satellite images have many applications, e.g. in meteorology, geography, education, cartography and warfare. They are an accurate and detailed depiction of the surface of the earth from above. Although it is relatively simple to collect many satellite images in an automated way, challenges arise when processing them for use in navigation and cartography. The idea of this project is to automatically convert an arbitrary satellite image, of e.g. a city, to a map of simple 2D shapes (streets, houses, forests) and label them with colors (semantic segmentation). The student will collect a dataset of satellite image and topological maps and train a deep neural network that learns to map from one domain to the other. The data could be obtained from a Google Maps database or similar.

New Variables of Brain Morphometry: the Potential and Limitations of CNN Regression

Timo blattner · sept. 2022.

The calculation of variables of brain morphology is computationally very expensive and time-consuming. A previous work showed the feasibility of ex- tracting the variables directly from T1-weighted brain MRI images using a con- volutional neural network. We used significantly more data and extended their model to a new set of neuromorphological variables, which could become inter- esting biomarkers in the future for the diagnosis of brain diseases. The model shows for nearly all subjects a less than 5% mean relative absolute error. This high relative accuracy can be attributed to the low morphological variance be- tween subjects and the ability of the model to predict the cortical atrophy age trend. The model however fails to capture all the variance in the data and shows large regional differences. We attribute these limitations in part to the moderate to poor reliability of the ground truth generated by FreeSurfer. We further investigated the effects of training data size and model complexity on this regression task and found that the size of the dataset had a significant impact on performance, while deeper models did not perform better. Lack of interpretability and dependence on a silver ground truth are the main drawbacks of this direct regression approach.

Home Monitoring by Radar

Lars ziegler · sept. 2022.

Detection and tracking of humans via UWB radars is a promising and continuously evolving field with great potential for medical technology. This contactless method of acquiring data of a patients movement patterns is ideal for in home application. As irregularities in a patients movement patterns are an indicator for various health problems including neurodegenerative diseases, the insight this data could provide may enable earlier detection of such problems. In this thesis a signal processing pipeline is presented with which a persons movement is modeled. During an experiment 142 measurements were recorded by two separate radar systems and one lidar system which each consisted of multiple sensors. The models that were calculated on these measurements by the signal processing pipeline were used to predict the times when a person stood up or sat down. The predictions showed an accuracy of 72.2%.

Revisiting non-learning based 3D reconstruction from multiple images

Aaron sägesser · oct. 2021.

Arthroscopy consists of challenging tasks and requires skills that even today, young surgeons still train directly throughout the surgery. Existing simulators are expensive and rarely available. Through the growing potential of virtual reality(VR) (head-mounted) devices for simulation and their applicability in the medical context, these devices have become a promising alternative that would be orders of magnitude cheaper and could be made widely available. To build a VR-based training device for arthroscopy is the overall aim of our project, as this would be of great benefit and might even be applicable in other minimally invasive surgery (MIS). This thesis marks a first step of the project with its focus to explore and compare well-known algorithms in a multi-view stereo (MVS) based 3D reconstruction with respect to imagery acquired by an arthroscopic camera. Simultaneously with this reconstruction, we aim to gain essential measures to compare the VR environment to the real world, as validation of the realism of future VR tasks. We evaluate 3 different feature extraction algorithms with 3 different matching techniques and 2 different algorithms for the estimation of the fundamental (F) matrix. The evaluation of these 18 different setups is made with a reconstruction pipeline embedded in a jupyter notebook implemented in python based on common computer vision libraries and compared with imagery generated with a mobile phone as well as with the reconstruction results of state-of-the-art (SOTA) structure-from-motion (SfM) software COLMAP and Multi-View Environment (MVE). Our comparative analysis manifests the challenges of heavy distortion, the fish-eye shape and weak image quality of arthroscopic imagery, as all results are substantially worse using this data. However, there are huge differences regarding the different setups. Scale Invariant Feature Transform (SIFT) and Oriented FAST Rotated BRIEF (ORB) in combination with k-Nearest Neighbour (kNN) matching and Least Median of Squares (LMedS) present the most promising results. Overall, the 3D reconstruction pipeline is a useful tool to foster the process of gaining measurements from the arthroscopic exploration device and to complement the comparative research in this context.

Examination of Unsupervised Representation Learning by Predicting Image Rotations

Eric lagger · sept. 2020.

In recent years deep convolutional neural networks achieved a lot of progress. To train such a network a lot of data is required and in supervised learning algorithms it is necessary that the data is labeled. To label data there is a lot of human work needed and this takes a lot of time and money to be done. To avoid the inconveniences that come with this we would like to find systems that don’t need labeled data and therefore are unsupervised learning algorithms. This is the importance of unsupervised algorithms, even though their outcome is not yet on the same qualitative level as supervised algorithms. In this thesis we will discuss an approach of such a system and compare the results to other papers. A deep convolutional neural network is trained to learn the rotations that have been applied to a picture. So we take a large amount of images and apply some simple rotations and the task of the network is to discover in which direction the image has been rotated. The data doesn’t need to be labeled to any category or anything else. As long as all the pictures are upside down we hope to find some high dimensional patterns for the network to learn.

StitchNet: Image Stitching using Autoencoders and Deep Convolutional Neural Networks

Maurice rupp · sept. 2019.

This thesis explores the prospect of artificial neural networks for image processing tasks. More specifically, it aims to achieve the goal of stitching multiple overlapping images to form a bigger, panoramic picture. Until now, this task is solely approached with ”classical”, hardcoded algorithms while deep learning is at most used for specific subtasks. This thesis introduces a novel end-to-end neural network approach to image stitching called StitchNet, which uses a pre-trained autoencoder and deep convolutional networks. Additionally to presenting several new datasets for the task of supervised image stitching with each 120’000 training and 5’000 validation samples, this thesis also conducts various experiments with different kinds of existing networks designed for image superresolution and image segmentation adapted to the task of image stitching. StitchNet outperforms most of the adapted networks in both quantitative as well as qualitative results.

Facial Expression Recognition in the Wild

Luca rolshoven · sept. 2019.

The idea of inferring the emotional state of a subject by looking at their face is nothing new. Neither is the idea of automating this process using computers. Researchers used to computationally extract handcrafted features from face images that had proven themselves to be effective and then used machine learning techniques to classify the facial expressions using these features. Recently, there has been a trend towards using deeplearning and especially Convolutional Neural Networks (CNNs) for the classification of these facial expressions. Researchers were able to achieve good results on images that were taken in laboratories under the same or at least similar conditions. However, these models do not perform very well on more arbitrary face images with different head poses and illumination. This thesis aims to show the challenges of Facial Expression Recognition (FER) in this wild setting. It presents the currently used datasets and the present state-of-the-art results on one of the biggest facial expression datasets currently available. The contributions of this thesis are twofold. Firstly, I analyze three famous neural network architectures and their effectiveness on the classification of facial expressions. Secondly, I present two modifications of one of these networks that lead to the proposed STN-COV model. While this model does not outperform all of the current state-of-the-art models, it does beat several ones of them.

A Study of 3D Reconstruction of Varying Objects with Deformable Parts Models

Raoul grossenbacher · july 2019.

This work covers a new approach to 3D reconstruction. In traditional 3D reconstruction one uses multiple images of the same object to calculate a 3D model by taking information gained from the differences between the images, like camera position, illumination of the images, rotation of the object and so on, to compute a point cloud representing the object. The characteristic trait shared by all these approaches is that one can almost change everything about the image, but it is not possible to change the object itself, because one needs to find correspondences between the images. To be able to use different instances of the same object, we used a 3D DPM model that can find different parts of an object in an image, thereby detecting the correspondences between the different pictures, which we then can use to calculate the 3D model. To take this theory to practise, we gave a 3D DPM model, which was trained to detect cars, pictures of different car brands, where no pair of images showed the same vehicle and used the detected correspondences and the Factorization Method to compute the 3D point cloud. This technique leads to a completely new approach in 3D reconstruction, because changing the object itself was never done before.

Motion deblurring in the wild replication and improvements

Alvaro juan lahiguera · jan. 2019, coma outcome prediction with convolutional neural networks, stefan jonas · oct. 2018, automatic correction of self-introduced errors in source code, sven kellenberger · aug. 2018, neural face transfer: training a deep neural network to face-swap, till nikolaus schnabel · july 2018.

This thesis explores the field of artificial neural networks with realistic looking visual outputs. It aims at morphing face pictures of a specific identity to look like another individual by only modifying key features, such as eye color, while leaving identity-independent features unchanged. Prior works have covered the topic of symmetric translation between two specific domains but failed to optimize it on faces where only parts of the image may be changed. This work applies a face masking operation to the output at training time, which forces the image generator to preserve colors while altering the face, fitting it naturally inside the unmorphed surroundings. Various experiments are conducted including an ablation study on the final setting, decreasing the baseline identity switching performance from 81.7% to 75.8 % whilst improving the average χ2 color distance from 0.551 to 0.434. The provided code-based software gives users easy access to apply this neural face swap to images and videos of arbitrary crop and brings Computer Vision one step closer to replacing Computer Graphics in this specific area.

A Study of the Importance of Parts in the Deformable Parts Model

Sammer puran · june 2017, self-similarity as a meta feature, lucas husi · april 2017, a study of 3d deformable parts models for detection and pose-estimation, simon jenni · march 2015, amodal leaf segmentation, nicolas maier · nov. 2023.

Plant phenotyping is the process of measuring and analyzing various traits of plants. It provides essential information on how genetic and environmental factors affect plant growth and development. Manual phenotyping is highly time-consuming; therefore, many computer vision and machine learning based methods have been proposed in the past years to perform this task automatically based on images of the plants. However, the publicly available datasets (in particular, of Arabidopsis thaliana) are limited in size and diversity, making them unsuitable to generalize to new unseen environments. In this work, we propose a complete pipeline able to automatically extract traits of interest from an image of Arabidopsis thaliana. Our method uses a minimal amount of existing annotated data from a source domain to generate a large synthetic dataset adapted to a different target domain (e.g., different backgrounds, lighting conditions, and plant layouts). In addition, unlike the source dataset, the synthetic one provides ground-truth annotations for the occluded parts of the leaves, which are relevant when measuring some characteristics of the plant, e.g., its total area. This synthetic dataset is then used to train a model to perform amodal instance segmentation of the leaves to obtain the total area, leaf count, and color of each plant. To validate our approach, we create a small dataset composed of manually annotated real images of Arabidopsis thaliana, which is used to assess the performance of the models.

Assessment of movement and pose in a hospital bed by ambient and wearable sensor technology in healthy subjects

Tony licata · sept. 2022.

The use of automated systems describing the human motion has become possible in various domains. Most of the proposed systems are designed to work with people moving around in a standing position. Because such system could be interesting in a medical environment, we propose in this work a pipeline that can effectively predict human motion from people lying on beds. The proposed pipeline is tested with a data set composed of 41 participants executing 7 predefined tasks in a bed. The motion of the participants is measured with video cameras, accelerometers and pressure mat. Various experiments are carried with the information retrieved from the data set. Two approaches combining the data from the different measure technologies are explored. The performance of the different carried experiments is measured, and the proposed pipeline is composed with components providing the best results. Later on, we show that the proposed pipeline only needs to use the video cameras, which make the proposed environment easier to implement in real life situations.

Machine Learning Based Prediction of Mental Health Using Wearable-measured Time Series

Seyedeh sharareh mirzargar · sept. 2022.

Depression is the second major cause for years spent in disability and has a growing prevalence in adolescents. The recent Covid-19 pandemic has intensified the situation and limited in-person patient monitoring due to distancing measures. Recent advances in wearable devices have made it possible to record the rest/activity cycle remotely with high precision and in real-world contexts. We aim to use machine learning methods to predict an individual's mental health based on wearable-measured sleep and physical activity. Predicting an impending mental health crisis of an adolescent allows for prompt intervention, detection of depression onset or its recursion, and remote monitoring. To achieve this goal, we train three primary forecasting models; linear regression, random forest, and light gradient boosted machine (LightGBM); and two deep learning models; block recurrent neural network (block RNN) and temporal convolutional network (TCN); on Actigraph measurements to forecast mental health in terms of depression, anxiety, sleepiness, stress, sleep quality, and behavioral problems. Our models achieve a high forecasting performance, the random forest being the winner to reach an accuracy of 98% for forecasting the trait anxiety. We perform extensive experiments to evaluate the models' performance in accuracy, generalization, and feature utilization, using a naive forecaster as the baseline. Our analysis shows minimal mental health changes over two months, making the prediction task easily achievable. Due to these minimal changes in mental health, the models tend to primarily use the historical values of mental health evaluation instead of Actigraph features. At the time of this master thesis, the data acquisition step is still in progress. In future work, we plan to train the models on the complete dataset using a longer forecasting horizon to increase the level of mental health changes and perform transfer learning to compensate for the small dataset size. This interdisciplinary project demonstrates the opportunities and challenges in machine learning based prediction of mental health, paving the way toward using the same techniques to forecast other mental disorders such as internalizing disorder, Parkinson's disease, Alzheimer's disease, etc. and improving the quality of life for individuals who have some mental disorder.

CNN Spike Detector: Detection of Spikes in Intracranial EEG using Convolutional Neural Networks

Stefan jonas · oct. 2021.

The detection of interictal epileptiform discharges in the visual analysis of electroencephalography (EEG) is an important but very difficult, tedious, and time-consuming task. There have been decades of research on computer-assisted detection algorithms, most recently focused on using Convolutional Neural Networks (CNNs). In this thesis, we present the CNN Spike Detector, a convolutional neural network to detect spikes in intracranial EEG. Our dataset of 70 intracranial EEG recordings from 26 subjects with epilepsy introduces new challenges in this research field. We report cross-validation results with a mean AUC of 0.926 (+- 0.04), an area under the precision-recall curve (AUPRC) of 0.652 (+- 0.10) and 12.3 (+- 7.47) false positive epochs per minute for a sensitivity of 80%. A visual examination of false positive segments is performed to understand the model behavior leading to a relatively high false detection rate. We notice issues with the evaluation measures and highlight a major limitation of the common approach of detecting spikes using short segments, namely that the network is not capable to consider the greater context of the segment with regards to its origination. For this reason, we present the Context Model, an extension in which the CNN Spike Detector is supplied with additional information about the channel. Results show promising but limited performance improvements. This thesis provides important findings about the spike detection task for intracranial EEG and lays out promising future research directions to develop a network capable of assisting experts in real-world clinical applications.

PolitBERT - Deepfake Detection of American Politicians using Natural Language Processing

Maurice rupp · april 2021.

This thesis explores the application of modern Natural Language Processing techniques to the detection of artificially generated videos of popular American politicians. Instead of focusing on detecting anomalies and artifacts in images and sounds, this thesis focuses on detecting irregularities and inconsistencies in the words themselves, opening up a new possibility to detect fake content. A novel, domain-adapted, pre-trained version of the language model BERT combined with several mechanisms to overcome severe dataset imbalances yielded the best quantitative as well as qualitative results. Additionally to the creation of the biggest publicly available dataset of English-speaking politicians consisting of 1.5 M sentences from over 1000 persons, this thesis conducts various experiments with different kinds of text classification and sequence processing algorithms applied to the political domain. Furthermore, multiple ablations to manage severe data imbalance are presented and evaluated.

A Study on the Inversion of Generative Adversarial Networks

Ramona beck · march 2021.

The desire to use generative adversarial networks (GANs) for real-world tasks such as object segmentation or image manipulation is increasing as synthesis quality improves, which has given rise to an emerging research area called GAN inversion that focuses on exploring methods for embedding real images into the latent space of a GAN. In this work, we investigate different GAN inversion approaches using an existing generative model architecture that takes a completely unsupervised approach to object segmentation and is based on StyleGAN2. In particular, we propose and analyze algorithms for embedding real images into the different latent spaces Z, W, and W+ of StyleGAN following an optimization-based inversion approach, while also investigating a novel approach that allows fine-tuning of the generator during the inversion process. Furthermore, we investigate a hybrid and a learning-based inversion approach, where in the former we train an encoder with embeddings optimized by our best optimization-based inversion approach, and in the latter we define an autoencoder, consisting of an encoder and the generator of our generative model as a decoder, and train it to map an image into the latent space. We demonstrate the effectiveness of our methods as well as their limitations through a quantitative comparison with existing inversion methods and by conducting extensive qualitative and quantitative experiments with synthetic data as well as real images from a complex image dataset. We show that we achieve qualitatively satisfying embeddings in the W and W+ spaces with our optimization-based algorithms, that fine-tuning the generator during the inversion process leads to qualitatively better embeddings in all latent spaces studied, and that the learning-based approach also benefits from a variable generator as well as a pre-training with our hybrid approach. Furthermore, we evaluate our approaches on the object segmentation task and show that both our optimization-based and our hybrid and learning-based methods are able to generate meaningful embeddings that achieve reasonable object segmentations. Overall, our proposed methods illustrate the potential that lies in the GAN inversion and its application to real-world tasks, especially in the relaxed version of the GAN inversion where the weights of the generator are allowed to vary.

Multi-scale Momentum Contrast for Self-supervised Image Classification

Zhao xueqi · dec. 2020.

With the maturity of supervised learning technology, people gradually shift the research focus to the field of self-supervised learning. ”Momentum Contrast” (MoCo) proposes a new self-supervised learning method and raises the correct rate of self-supervised learning to a new level. Inspired by another article ”Representation Learning by Learning to Count”, if a picture is divided into four parts and passed through a neural network, it is possible to further improve the accuracy of MoCo. Different from the original MoCo, this MoCo variant (Multi-scale MoCo) does not directly pass the image through the encoder after the augmented images. Multi-scale MoCo crops and resizes the augmented images, and the obtained four parts are respectively passed through the encoder and then summed (upsampled version do not do resize to input but resize the contrastive samples). This method of images crop is not only used for queue q but also used for comparison queue k, otherwise the weights of queue k might be damaged during the moment update. This will further discussed in the experiments chapter between downsampled Multi-scale version and downsampled both Multi-scale version. Human beings also have the same principle of object recognition: when human beings see something they are familiar with, even if the object is not fully displayed, people can still guess the object itself with a high probability. Because of this, Multi-scale MoCo applies this concept to the pretext part of MoCo, hoping to obtain better feature extraction. In this thesis, there are three versions of Multi-scale MoCo, downsampled input samples version, downsampled input samples and contrast samples version and upsampled input samples version. The differences between these versions will be described in more detail later. The neural network architecture comparison includes ResNet50 , and the tested data set is STL-10. The weights obtained in pretext will be transferred to self-supervised learning, and in the process of self-supervised learning, the weights of other layers except the final linear layer are frozen without changing (these weights come from pretext).

Self-Supervised Learning Using Siamese Networks and Binary Classifier

Dušan mihajlov · march 2020.

In this thesis, we present several approaches for training a convolutional neural network using only unlabeled data. Our autonomously supervised learning algorithms are based on connections between image patch i. e. zoomed image and its original. Using the siamese architecture neural network we aim to recognize, if the image patch, which is input to the first neural network part, comes from the same image presented to the second neural network part. By applying transformations to both images, and different zoom sizes at different positions, we force the network to extract high level features using its convolutional layers. At the top of our siamese architecture, we have a simple binary classifier that measures the difference between feature maps that we extract and makes a decision. Thus, the only way that the classifier will solve the task correctly is when our convolutional layers are extracting useful representations. Those representations we can than use to solve many different tasks that are related to the data used for unsupervised training. As the main benchmark for all of our models, we used STL10 dataset, where we train a linear classifier on the top of our convolutional layers with a small amount of manually labeled images, which is a widely used benchmark for unsupervised learning tasks. We also combine our idea with recent work on the same topic, and the network called RotNet, which makes use of image rotations and therefore forces the network to learn rotation dependent features from the dataset. As a result of this combination we create a new procedure that outperforms original RotNet.

Learning Object Representations by Mixing Scenes

Lukas zbinden · may 2019.

In the digital age of ever increasing data amassment and accessibility, the demand for scalable machine learning models effective at refining the new oil is unprecedented. Unsupervised representation learning methods present a promising approach to exploit this invaluable yet unlabeled digital resource at scale. However, a majority of these approaches focuses on synthetic or simplified datasets of images. What if a method could learn directly from natural Internet-scale image data? In this thesis, we propose a novel approach for unsupervised learning of object representations by mixing natural image scenes. Without any human help, our method mixes visually similar images to synthesize new realistic scenes using adversarial training. In this process the model learns to represent and understand the objects prevalent in natural image data and makes them available for downstream applications. For example, it enables the transfer of objects from one scene to another. Through qualitative experiments on complex image data we show the effectiveness of our method along with its limitations. Moreover, we benchmark our approach quantitatively against state-of-the-art works on the STL-10 dataset. Our proposed method demonstrates the potential that lies in learning representations directly from natural image data and reinforces it as a promising avenue for future research.

Representation Learning using Semantic Distances

Markus roth · may 2019, zero-shot learning using generative adversarial networks, hamed hemati · dec. 2018, dimensionality reduction via cnns - learning the distance between images, ioannis glampedakis · sept. 2018, learning to play othello using deep reinforcement learning and self play, thomas simon steinmann · sept. 2018, aba-j interactive multi-modality tissue sectionto-volume alignment: a brain atlasing toolkit for imagej, felix meyenhofer · march 2018, learning visual odometry with recurrent neural networks, adrian wälchli · feb. 2018.

In computer vision, Visual Odometry is the problem of recovering the camera motion from a video. It is related to Structure from Motion, the problem of reconstructing the 3D geometry from a collection of images. Decades of research in these areas have brought successful algorithms that are used in applications like autonomous navigation, motion capture, augmented reality and others. Despite the success of these prior works in real-world environments, their robustness is highly dependent on manual calibration and the magnitude of noise present in the images in form of, e.g., non-Lambertian surfaces, dynamic motion and other forms of ambiguity. This thesis explores an alternative approach to the Visual Odometry problem via Deep Learning, that is, a specific form of machine learning with artificial neural networks. It describes and focuses on the implementation of a recent work that proposes the use of Recurrent Neural Networks to learn dependencies over time due to the sequential nature of the input. Together with a convolutional neural network that extracts motion features from the input stream, the recurrent part accumulates knowledge from the past to make camera pose estimations at each point in time. An analysis on the performance of this system is carried out on real and synthetic data. The evaluation covers several ways of training the network as well as the impact and limitations of the recurrent connection for Visual Odometry.

Crime location and timing prediction

Bernard swart · jan. 2018, from cartoons to real images: an approach to unsupervised visual representation learning, simon jenni · feb. 2017, automatic and large-scale assessment of fluid in retinal oct volume, nina mujkanovic · dec. 2016, segmentation in 3d using eye-tracking technology, michele wyss · july 2016, accurate scale thresholding via logarithmic total variation prior, remo diethelm · aug. 2014, novel techniques for robust and generalizable machine learning, abdelhak lemkhenter · sept. 2023.

Neural networks have transcended their status of powerful proof-of-concept machine learning into the realm of a highly disruptive technology that has revolutionized many quantitative fields such as drug discovery, autonomous vehicles, and machine translation. Today, it is nearly impossible to go a single day without interacting with a neural network-powered application. From search engines to on-device photo-processing, neural networks have become the go-to solution thanks to recent advances in computational hardware and an unprecedented scale of training data. Larger and less curated datasets, typically obtained through web crawling, have greatly propelled the capabilities of neural networks forward. However, this increase in scale amplifies certain challenges associated with training such models. Beyond toy or carefully curated datasets, data in the wild is plagued with biases, imbalances, and various noisy components. Given the larger size of modern neural networks, such models run the risk of learning spurious correlations that fail to generalize beyond their training data. This thesis addresses the problem of training more robust and generalizable machine learning models across a wide range of learning paradigms for medical time series and computer vision tasks. The former is a typical example of a low signal-to-noise ratio data modality with a high degree of variability between subjects and datasets. There, we tailor the training scheme to focus on robust patterns that generalize to new subjects and ignore the noisier and subject-specific patterns. To achieve this, we first introduce a physiologically inspired unsupervised training task and then extend it by explicitly optimizing for cross-dataset generalization using meta-learning. In the context of image classification, we address the challenge of training semi-supervised models under class imbalance by designing a novel label refinement strategy with higher local sensitivity to minority class samples while preserving the global data distribution. Lastly, we introduce a new Generative Adversarial Networks training loss. Such generative models could be applied to improve the training of subsequent models in the low data regime by augmenting the dataset using generated samples. Unfortunately, GAN training relies on a delicate balance between its components, making it prone mode collapse. Our contribution consists of defining a more principled GAN loss whose gradients incentivize the generator model to seek out missing modes in its distribution. All in all, this thesis tackles the challenge of training more robust machine learning models that can generalize beyond their training data. This necessitates the development of methods specifically tailored to handle the diverse biases and spurious correlations inherent in the data. It is important to note that achieving greater generalizability in models goes beyond simply increasing the volume of data; it requires meticulous consideration of training objectives and model architecture. By tackling these challenges, this research contributes to advancing the field of machine learning and underscores the significance of thoughtful design in obtaining more resilient and versatile models.

Automated Sleep Scoring, Deep Learning and Physician Supervision

Luigi fiorillo · oct. 2022.

Sleep plays a crucial role in human well-being. Polysomnography is used in sleep medicine as a diagnostic tool, so as to objectively analyze the quality of sleep. Sleep scoring is the procedure of extracting sleep cycle information from the wholenight electrophysiological signals. The scoring is done worldwide by the sleep physicians according to the official American Academy of Sleep Medicine (AASM) scoring manual. In the last decades, a wide variety of deep learning based algorithms have been proposed to automatise the sleep scoring task. In this thesis we study the reasons why these algorithms fail to be introduced in the daily clinical routine, with the perspective of bridging the existing gap between the automatic sleep scoring models and the sleep physicians. In this light, the primary step is the design of a simplified sleep scoring architecture, also providing an estimate of the model uncertainty. Beside achieving results on par with most up-to-date scoring systems, we demonstrate the efficiency of ensemble learning based algorithms, together with label smoothing techniques, in both enhancing the performance and calibrating the simplified scoring model. We introduced an uncertainty estimate procedure, so as to identify the most challenging sleep stage predictions, and to quantify the disagreement between the predictions given by the model and the annotation given by the physicians. In this thesis we also propose a novel method to integrate the inter-scorer variability into the training procedure of a sleep scoring model. We clearly show that a deep learning model is able to encode this variability, so as to better adapt to the consensus of a group of scorers-physicians. We finally address the generalization ability of a deep learning based sleep scoring system, further studying its resilience to the sleep complexity and to the AASM scoring rules. We can state that there is no need to train the algorithm strictly following the AASM guidelines. Most importantly, using data from multiple data centers results in a better performing model compared with training on a single data cohort. The variability among different scorers and data centers needs to be taken into account, more than the variability among sleep disorders.

Learning Representations for Controllable Image Restoration

Givi meishvili · march 2022.

Deep Convolutional Neural Networks have sparked a renaissance in all the sub-fields of computer vision. Tremendous progress has been made in the area of image restoration. The research community has pushed the boundaries of image deblurring, super-resolution, and denoising. However, given a distorted image, most existing methods typically produce a single restored output. The tasks mentioned above are inherently ill-posed, leading to an infinite number of plausible solutions. This thesis focuses on designing image restoration techniques capable of producing multiple restored results and granting users more control over the restoration process. Towards this goal, we demonstrate how one could leverage the power of unsupervised representation learning. Image restoration is vital when applied to distorted images of human faces due to their social significance. Generative Adversarial Networks enable an unprecedented level of generated facial details combined with smooth latent space. We leverage the power of GANs towards the goal of learning controllable neural face representations. We demonstrate how to learn an inverse mapping from image space to these latent representations, tuning these representations towards a specific task, and finally manipulating latent codes in these spaces. For example, we show how GANs and their inverse mappings enable the restoration and editing of faces in the context of extreme face super-resolution and the generation of novel view sharp videos from a single motion-blurred image of a face. This thesis also addresses more general blind super-resolution, denoising, and scratch removal problems, where blur kernels and noise levels are unknown. We resort to contrastive representation learning and first learn the latent space of degradations. We demonstrate that the learned representation allows inference of ground-truth degradation parameters and can guide the restoration process. Moreover, it enables control over the amount of deblurring and denoising in the restoration via manipulation of latent degradation features.

Learning Generalizable Visual Patterns Without Human Supervision

Simon jenni · oct. 2021.

Owing to the existence of large labeled datasets, Deep Convolutional Neural Networks have ushered in a renaissance in computer vision. However, almost all of the visual data we generate daily - several human lives worth of it - remains unlabeled and thus out of reach of today’s dominant supervised learning paradigm. This thesis focuses on techniques that steer deep models towards learning generalizable visual patterns without human supervision. Our primary tool in this endeavor is the design of Self-Supervised Learning tasks, i.e., pretext-tasks for which labels do not involve human labor. Besides enabling the learning from large amounts of unlabeled data, we demonstrate how self-supervision can capture relevant patterns that supervised learning largely misses. For example, we design learning tasks that learn deep representations capturing shape from images, motion from video, and 3D pose features from multi-view data. Notably, these tasks’ design follows a common principle: The recognition of data transformations. The strong performance of the learned representations on downstream vision tasks such as classiﬁcation, segmentation, action recognition, or pose estimation validate this pretext-task design. This thesis also explores the use of Generative Adversarial Networks (GANs) for unsupervised representation learning. Besides leveraging generative adversarial learning to deﬁne image transformation for self-supervised learning tasks, we also address training instabilities of GANs through the use of noise. While unsupervised techniques can signiﬁcantly reduce the burden of supervision, in the end, we still rely on some annotated examples to ﬁne-tune learned representations towards a target task. To improve the learning from scarce or noisy labels, we describe a supervised learning algorithm with improved generalization in these challenging settings.

Learning Interpretable Representations of Images

Attila szabó · june 2019.

Computers represent images with pixels and each pixel contains three numbers for red, green and blue colour values. These numbers are meaningless for humans and they are mostly useless when used directly with classical machine learning techniques like linear classifiers. Interpretable representations are the attributes that humans understand: the colour of the hair, viewpoint of a car or the 3D shape of the object in the scene. Many computer vision tasks can be viewed as learning interpretable representations, for example a supervised classification algorithm directly learns to represent images with their class labels. In this work we aim to learn interpretable representations (or features) indirectly with lower levels of supervision. This approach has the advantage of cost savings on dataset annotations and the flexibility of using the features for multiple follow-up tasks. We made contributions in three main areas: weakly supervised learning, unsupervised learning and 3D reconstruction. In the weakly supervised case we use image pairs as supervision. Each pair shares a common attribute and differs in a varying attribute. We propose a training method that learns to separate the attributes into separate feature vectors. These features then are used for attribute transfer and classification. We also show theoretical results on the ambiguities of the learning task and the ways to avoid degenerate solutions. We show a method for unsupervised representation learning, that separates semantically meaningful concepts. We explain and show ablation studies how the components of our proposed method work: a mixing autoencoder, a generative adversarial net and a classifier. We propose a method for learning single image 3D reconstruction. It is done using only the images, no human annotation, stereo, synthetic renderings or ground truth depth map is needed. We train a generative model that learns the 3D shape distribution and an encoder to reconstruct the 3D shape. For that we exploit the notion of image realism. It means that the 3D reconstruction of the object has to look realistic when it is rendered from different random angles. We prove the efficacy of our method from first principles.

Learning Controllable Representations for Image Synthesis

Qiyang hu · june 2019.

In this thesis, our focus is learning a controllable representation and applying the learned controllable feature representation on images synthesis, video generation, and even 3D reconstruction. We propose different methods to disentangle the feature representation in neural network and analyze the challenges in disentanglement such as reference ambiguity and shortcut problem when using the weak label. We use the disentangled feature representation to transfer attributes between images such as exchanging hairstyle between two face images. Furthermore, we study the problem of how another type of feature, sketch, works in a neural network. The sketch can provide shape and contour of an object such as the silhouette of the side-view face. We leverage the silhouette constraint to improve the 3D face reconstruction from 2D images. The sketch can also provide the moving directions of one object, thus we investigate how one can manipulate the object to follow the trajectory provided by a user sketch. We propose a method to automatically generate video clips from a single image input using the sketch as motion and trajectory guidance to animate the object in that image. We demonstrate the efficiency of our approaches on several synthetic and real datasets.

Beyond Supervised Representation Learning

Mehdi noroozi · jan. 2019.

The complexity of any information processing task is highly dependent on the space where data is represented. Unfortunately, pixel space is not appropriate for the computer vision tasks such as object classification. The traditional computer vision approaches involve a multi-stage pipeline where at first images are transformed to a feature space through a handcrafted function and then consequenced by the solution in the feature space. The challenge with this approach is the complexity of designing handcrafted functions that extract robust features. The deep learning based approaches address this issue by end-to-end training of a neural network for some tasks that lets the network to discover the appropriate representation for the training tasks automatically. It turns out that image classification task on large scale annotated datasets yields a representation transferable to other computer vision tasks. However, supervised representation learning is limited to annotations. In this thesis we study self-supervised representation learning where the goal is to alleviate these limitations by substituting the classification task with pseudo tasks where the labels come for free. We discuss self-supervised learning by solving jigsaw puzzles that uses context as supervisory signal. The rational behind this task is that the network requires to extract features about object parts and their spatial configurations to solve the jigsaw puzzles. We also discuss a method for representation learning that uses an artificial supervisory signal based on counting visual primitives. This supervisory signal is obtained from an equivariance relation. We use two image transformations in the context of counting: scaling and tiling. The first transformation exploits the fact that the number of visual primitives should be invariant to scale. The second transformation allows us to equate the total number of visual primitives in each tile to that in the whole image. The most effective transfer strategy is fine-tuning, which restricts one to use the same model or parts thereof for both pretext and target tasks. We discuss a novel framework for self-supervised learning that overcomes limitations in designing and comparing different tasks, models, and data domains. In particular, our framework decouples the structure of the self-supervised model from the final task-specific finetuned model. Finally, we study the problem of multi-task representation learning. A naive approach to enhance the representation learned by a task is to train the task jointly with other tasks that capture orthogonal attributes. Having a diverse set of auxiliary tasks, imposes challenges on multi-task training from scratch. We propose a framework that allows us to combine arbitrarily different feature spaces into a single deep neural network. We reduce the auxiliary tasks to classification tasks and the multi-task learning to multi-label classification task consequently. Nevertheless, combining multiple representation space without being aware of the target task might be suboptimal. As our second contribution, we show empirically that this is indeed the case and propose to combine multiple tasks after the fine-tuning on the target task.

Motion Deblurring from a Single Image

Meiguang jin · dec. 2018.

With the information explosion, a tremendous amount photos is captured and shared via social media everyday. Technically, a photo requires a finite exposure to accumulate light from the scene. Thus, objects moving during the exposure generate motion blur in a photo. Motion blur is an image degradation that makes visual content less interpretable and is therefore often seen as a nuisance. Although motion blur can be reduced by setting a short exposure time, an insufficient amount of light has to be compensated through increasing the sensor’s sensitivity, which will inevitably bring large amount of sensor noise. Thus this motivates the necessity of removing motion blur computationally. Motion deblurring is an important problem in computer vision and it is challenging due to its ill-posed nature, which means the solution is not well defined. Mathematically, a blurry image caused by uniform motion is formed by the convolution operation between a blur kernel and a latent sharp image. Potentially there are infinite pairs of blur kernel and latent sharp image that can result in the same blurry image. Hence, some prior knowledge or regularization is required to address this problem. Even if the blur kernel is known, restoring the latent sharp image is still difficult as the high frequency information has been removed. Although we can model the uniform motion deblurring problem mathematically, it can only address the camera in-plane translational motion. Practically, motion is more complicated and can be non-uniform. Non-uniform motion blur can come from many sources, camera out-of-plane rotation, scene depth change, object motion and so on. Thus, it is more challenging to remove non-uniform motion blur. In this thesis, our focus is motion blur removal. We aim to address four challenging motion deblurring problems. We start from the noise blind image deblurring scenario where blur kernel is known but the noise level is unknown. We introduce an efficient and robust solution based on a Bayesian framework using a smooth generalization of the 0−1 loss to address this problem. Then we study the blind uniform motion deblurring scenario where both the blur kernel and the latent sharp image are unknown. We explore the relative scale ambiguity between the latent sharp image and blur kernel to address this issue. Moreover, we study the face deblurring problem and introduce a novel deep learning network architecture to solve it. We also address the general motion deblurring problem and particularly we aim at recovering a sequence of 7 frames each depicting some instantaneous motion of the objects in the scene.

Towards a Novel Paradigm in Blind Deconvolution: From Natural to Cartooned Image Statistics

Daniele perrone · july 2015.

In this thesis we study the blind deconvolution problem. Blind deconvolution consists in the estimation of a sharp image and a blur kernel from an observed blurry image. Because the blur model admits several solutions it is necessary to devise an image prior that favors the true blur kernel and sharp image. Recently it has been shown that a class of blind deconvolution formulations and image priors has the no-blur solution as global minimum. Despite this shortcoming, algorithms based on these formulations and priors can successfully solve blind deconvolution. In this thesis we show that a suitable initialization can exploit the non-convexity of the problem and yield the desired solution. Based on these conclusions, we propose a novel “vanilla” algorithm stripped of any enhancement typically used in the literature. Our algorithm, despite its simplicity, is able to compete with the top performers on several datasets. We have also investigated a remarkable behavior of a 1998 algorithm, whose formulation has the no-blur solution as global minimum: even when initialized at the no-blur solution, it converges to the correct solution. We show that this behavior is caused by an apparently insignificant implementation strategy that makes the algorithm no longer minimize the original cost functional. We also demonstrate that this strategy improves the results of our “vanilla” algorithm. Finally, we present a study of image priors for blind deconvolution. We provide experimental evidence supporting the recent belief that a good image prior is one that leads to a good blur estimate rather than being a good natural image statistical model. By focusing the attention on the blur estimation alone, we show that good blur estimates can be obtained even when using images quite different from the true sharp image. This allows using image priors, such as those leading to “cartooned” images, that avoid the no-blur solution. By using an image prior that produces “cartooned” images we achieve state-of-the-art results on different publicly available datasets. We therefore suggests a shift of paradigm in blind deconvolution: from modeling natural image statistics to modeling cartooned image statistics.

New Perspectives on Uncalibrated Photometric Stereo

Thoma papadhimitri · june 2014.

This thesis investigates the problem of 3D reconstruction of a scene from 2D images. In particular, we focus on photometric stereo which is a technique that computes the 3D geometry from at least three images taken from the same viewpoint and under different illumination conditions. When the illumination is unknown (uncalibrated photometric stereo) the problem is ambiguous: different combinations of geometry and illumination can generate the same images. First, we solve the ambiguity by exploiting the Lambertian reflectance maxima. These are points defined on curved surfaces where the normals are parallel to the light direction. Then, we propose a solution that can be computed in closed-form and thus very efficiently. Our algorithm is also very robust and yields always the same estimate regardless of the initial ambiguity. We validate our method on real world experiments and achieve state-of-art results. In this thesis we also solve for the first time the uncalibrated photometric stereo problem under the perspective projection model. We show that unlike in the orthographic case, one can uniquely reconstruct the normals of the object and the lights given only the input images and the camera calibration (focal length and image center). We also propose a very efficient algorithm which we validate on synthetic and real world experiments and show that the proposed technique is a generalization of the orthographic case. Finally, we investigate the uncalibrated photometric stereo problem in the case where the lights are distributed near the scene. In this case we propose an alternating minimization technique which converges quickly and overcomes the limitations of prior work that assumes distant illumination. We show experimentally that adopting a near-light model for real world scenes yields very accurate reconstructions.

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Digital image processing

Masters Thesis
Ha, Vinh Thuc
Wong, Robert
Bavarian, Behzad
Electrical and Computer Engineering
California State University, Northridge
Dissertations, Academic -- CSUN -- Engineering.
2017-04-11T18:16:09Z
http://hdl.handle.net/10211.3/189576
by Vinh Thuc Ha
California State University, Northridge. Department of Engineering.
Includes bibliographical references (page 59)

[email protected]
+91-97 91 62 64 69

Digital Image Processing Thesis

Digital Image Processing is a technique to work on the set of pixels/bits in a digital image to produce the image in another useful format and it is shortly denoted as DIP. This manipulation of the image will bring a new dimension to the image. Most importantly, it has several unique characteristics that give tremendous creative topics. This page is about the new innovation Digital Image Processing Thesis ideas with important research areas!!!

Characteristics of Digital Image Processing

Minimize the complication in processing image
Assure image quality through noise-free images
Broadly integrated with other promising fields
Provide better experience and visual interpretation
Enhance the weakened or unclear image content
Supports various open-source software (paid / free)

DIP approach analyzes the image from different angles to give the various perception of the image. Also, it is used to i mprove the quality of the image and extract the essential features of the image through well-defined procedures. Further, we have mentioned the merits of image processing for your knowledge.

Advantages of Digital Image Processing

Rapid Image Acquisition, Retrieval, and Storage
Eye Controlled Viewing (Zooming and Windowing)
3D Image Reformatting (Multi-view and Multi-plane)
Quick Image Distribution without compromising quality
Hybrid Image Reconstruction (CT, PET, MRI, SPECT, PET/CT, SPECT/CT)

Introduction to Image Processing

Generally, image processing is the practice of manipulating images through different techniques to generate an enhanced new image . Further, it is also referred to as signal processing which yields image/ image feature as output after processing input image.

Input: Features
Output: Understanding
Examples: Autonomous Driving and Scene Parsing
Input: Image
Output: Image
Examples: Image Sharpening and De-noising
Output: Features
Examples: Image Segmentation and Object Identification

Due to the flexibility in image processing, this field is widely growing in all the leading research areas and applications . On the whole, it becomes the central research field in information and computer science engineering disciplines. Her, we have listed out a few latest Digital Image Processing Thesis ideas .

Research Topics in Digital Image Processing

Remote Ultra-High Resolution Image Formation
3D Image Display, Printing, and Scanning
High-Speed Video / Image Synthesis and Processing
3 Dimensional Image Display and Acquisition
Industrial Ultrasound Image and Signal Processing Applications

Majorly, DIP involves two main operations in all the applications. Firstly, it manipulates the image for better computer vision which includes representation and storage. Secondly, it enhances the image quality for human understanding . Also, it falls under any of the below frequencies.

How do frequencies show up in an image?

High frequencies – High variation in pixel intensities (For example edges)
Low frequencies – Slow variation in pixel intensities (For instance: continuous surface)

What is Digital Image Processing (DIP)?

Digital Image Processing (DIP) is mathematical operations enabled software which mainly designed to process the computer stored digital images . Through this technique, we can manipulate the images in all aspects to get the fine-tuned information of the image . We guide research scholars to choose interesting digital image processing thesis topics . For instance: MATLAB, Adobe Photoshop, etc. For your reference, here, we have given the high demanding research areas for the current DIP PhD / MS research study.

Types of an image

Black and White Image – The black and white image is exactly referred to as grayscale image which a pixel value may be either absolute white or absolute black
8-bit Color Format – It is also a grayscale image with a different range of values. Here, each pixel represents 8bit color which can display a maximum of 256 different shades of colors. (1 – white, 127 – gray, 255 – back)
Binary Image – The binary image is also referred to as Monochrome which contains only a 2-pixel intensity value i.e., 1and 0. (1 – white and 0 – black)
16-bit Color Format – It is a High Color Format with 65,536 colors variation. Compare to the grayscale, it has a different format for color distribution

What are the steps in digital image processing?

Color Space Image Processing
Image Acquisition and Compression
Object Recognition and Classification
Image Enhancement and Restoration
Wavelets and Multi-resolution Processing
Morphological Processing and Segmentation
Knowledge Representation and Description

Further, it is also helpful to enhance the images, view the invisible data, detect the specific object, reconstruct the damaged image, extract the special features , etc. By the by, it is also called digital signal processing which is a covert signal from the image sensor to digital images. Also, we have given the different types of images that we use for processing the image.

Research Areas in Digital Image Processing

Recently, our resource team has handpicked the unique research areas for digital image processing thesis. The below areas are very significant to begin your research career. These areas surely shape your knowledge to create a strong foundation in your profession.

Industrial Applications
Human-Computer / Machine Interfaces
Artistic Impact on Image
Video and Image Processing Architecture
Medical Image Visualization and Inspection
Image Restoration and Quality Improvement
Insight of the image content (Computer Vision)
Fast Video or Image Labeling, Distributing and Retrieval
Creation and Synthesis of images (Computer Graphics)

In addition to research and development services, we also give our support in Digital Image Processing Thesis writing. We have a team of native writers to help you in preparing a flawless master thesis. For the best thesis writing service , we follow certain policies in writing your thesis. For your information, we have given the list of criteria that we include in developing the best thesis to publish your work in latest image processing journals list .

Thesis Writing Format

Give a brief overview of the research proposal
Elaborate the handpicked research question/problem
Analyze the theoretical context
Review the related topics from recent history
Find whether someone makes a debate on gaps
Identify the research gaps in the selected area
Survey on problem-solving methods in related works
Ethics Statement
Design the system architecture
Mention what type of data is used as input
Give the summary flow of research
Analyze the required costs for execution
Access and Select the optimal ones
Acquire the proposed algorithms and methods
Human Subjects Assessment
Give an explanation on primary findings
Analyze and reveal the weakness
Notify the approaches to be followed
Give the proofs or evidence for the research need
Give information about the research scope
Make a note on relations and categories
Define the limitation statement
Specify the suitable alternatives
Describe the significance of the research
Mention the contribution given to the field through research

Overall, if you are looking best end-to-end research support in the Digital Image Processing Thesis Writing , then you can find it as the one-stop solution. We will give our top-quality research service to attain the finest research outcome.

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Research Topics

Biomedical Imaging

The current plethora of imaging technologies such as magnetic resonance imaging (MR), computed tomography (CT), position emission tomography (PET), optical coherence tomography (OCT), and ultrasound provide great insight into the different anatomical and functional processes of the human body.

Computer Vision

Computer vision is the science and technology of teaching a computer to interpret images and video as well as a typical human. Technically, computer vision encompasses the fields of image/video processing, pattern recognition, biological vision, artificial intelligence, augmented reality, mathematical modeling, statistics, probability, optimization, 2D sensors, and photography.

Image Segmentation/Classification

Extracting information from a digital image often depends on first identifying desired objects or breaking down the image into homogenous regions (a process called 'segmentation') and then assigning these objects to particular classes (a process called 'classification'). This is a fundamental part of computer vision, combining image processing and pattern recognition techniques.

Multiresolution Techniques

The VIP lab has a particularly extensive history with multiresolution methods, and a significant number of research students have explored this theme. Multiresolution methods are very broad, essentially meaning than an image or video is modeled, represented, or features extracted on more than one scale, somehow allowing both local and non-local phenomena.

Remote Sensing

Remote sensing, or the science of capturing data of the earth from airplanes or satellites, enables regular monitoring of land, ocean, and atmosphere expanses, representing data that cannot be captured using any other means. A vast amount of information is generated by remote sensing platforms and there is an obvious need to analyze the data accurately and efficiently.

Scientific Imaging

Scientific Imaging refers to working on two- or three-dimensional imagery taken for a scientific purpose, in most cases acquired either through a microscope or remotely-sensed images taken at a distance.

Stochastic Models

In many image processing, computer vision, and pattern recognition applications, there is often a large degree of uncertainty associated with factors such as the appearance of the underlying scene within the acquired data, the location and trajectory of the object of interest, the physical appearance (e.g., size, shape, color, etc.) of the objects being detected, etc.

Video Analysis

Video analysis is a field within computer vision that involves the automatic interpretation of digital video using computer algorithms. Although humans are readily able to interpret digital video, developing algorithms for the computer to perform the same task has been highly evasive and is now an active research field.

Evolutionary Deep Intelligence

Deep learning has shown considerable promise in recent years, producing tremendous results and significantly improving the accuracy of a variety of challenging problems when compared to other machine learning methods.

Discovery Radiomics

Radiomics, which involves the high-throughput extraction and analysis of a large amount of quantitative features from medical imaging data to characterize tumor phenotype in a quantitative manner, is ushering in a new era of imaging-driven quantitative personalized cancer decision support and management.

Sports Analytics

Sports Analytics is a growing field in computer vision that analyzes visual cues from images to provide statistical data on players, teams, and games. Want to know how a player's technique improves the quality of the team? Can a team, based on their defensive position, increase their chances to the finals? These are a few out of a plethora of questions that are answered in sports analytics.

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Image Processing Based Project Topics With Abstracts and Base Papers 2024

Embark on a visual journey into the realm of image processing with our meticulously curated selection of M.Tech project topics for 2024, thoughtfully paired with trending IEEE base papers. These projects encapsulate the forefront of visual innovation, serving as an indispensable resource for M.Tech students keen on exploring the dynamic landscape of image analysis and manipulation. Our comprehensive compilation spans a diverse range of Image Processing project topics, each complemented by a carefully chosen base paper and a concise abstract. From computer vision and pattern recognition to deep learning techniques and medical image processing, these projects represent the latest trends in the ever-evolving field of visual computing. Stay ahead of the technological curve by delving into projects that align with the current demands and challenges faced by industries globally. Whether you’re a student, researcher, or industry professional, our collection acts as a gateway to the cutting-edge advancements in visual innovation. The project titles are strategically chosen to incorporate keywords that resonate with the latest IEEE standards and technological breakthroughs, ensuring relevance and alignment with industry needs. Explore the abstracts to quickly grasp the scope, methodologies, and potential impacts of each project.

M.Tech Projects Topics List In Image Processing Based

Top 5 Image Processing Projects Ideas & Topics [For Beginners]

In this blog, we will walk through the introduction of image processing and then proceed to talk about a few project ideas that revolve around image processing.

Image processing is a technique used to perform some operations on the image in order to obtain some meaningful information from them. Here, the input will be an image and after applying a few operations we get an enhanced image or some features associated with these images.

In image processing, an image is considered as a two-dimensional array of numbers ranging from 0 to 255. Image compression, sharpening, edge-detection are all achieved by using special filters and operators that transform the input image to the output we wish to achieve. For instance, for brightening the image, the operator or filter will behave in a manner that would increase the pixel value of the image.

These operators perform mathematical operations with the 2-D array and produce a new set of output arrays with the desired result. These operations are being extensively used in domains like, Computer vision and Artificial Intelligence , and Machine learning.

With time, image processing is gaining importance in several sectors. Many industries have leveraged the importance of image recognition and the market is anticipated to experience a CAGR of 15.1% throughout the forecast period, aiming to achieve a market value of USD 53.0 billion by 2025.

Moving on, now that we have a basic understanding of what is image processing let us dive into some of the project ideas that can be created by leveraging the aforementioned concept on image processing.

Why Should You Use Image Processing Techniques?

Image restoration and processing are pivotal in several applications across various domains. They offer a multitude of advantages that significantly contribute to the understanding and improvement of visual data. One fundamental reason for leveraging image restoration and processing techniques is visual quality enhancement. These techniques can refine and clarify images through sophisticated algorithms and methodologies, making them visually appealing and facilitating a more detailed analysis.

In addition to visual enhancement, image processing allows for effective noise reduction, addressing distortions and unwanted elements that may compromise the accuracy and reliability of visual data. By employing advanced filtering and smoothing techniques, image processing mitigates noise, ensuring that the underlying information in the image remains prominent and uncorrupted. This is particularly crucial in applications such as medical imaging, where accurate diagnosis depends on the clarity and fidelity of visual representations.

Pattern recognition stands out as another compelling reason to delve into image processing. By enabling systems to recognize patterns and objects within images, image processing opens the door to a plethora of applications. From facial recognition in biometric systems to object detection in autonomous vehicles, the ability to identify and understand visual patterns forms the backbone of cutting-edge technologies.

The automation of visual tasks, facilitated by image processing algorithms, enhances efficiency and reduces manual efforts and time involved in manufacturing processes. This not only streamlines operations but also contributes to improved quality control by enabling the identification of defects and irregularities in real time.

Moreover, image restoration techniques prove invaluable in the preservation of cultural heritage. Whether restoring old photographs, manuscripts, or artifacts, image restoration ensures the longevity of these historical treasures. By removing imperfections, enhancing colors, and preserving original details, image restoration contributes to conserving and appreciating cultural artifacts for future generations.

The role of image processing extends to security and surveillance, where improved identification of objects and individuals is paramount. Through advanced image processing, security systems can accurately monitor and analyze visual data in real-time, enhancing overall safety in public spaces, airports, and critical infrastructure.

The deployment of image restoration and processing techniques brings about innovation and exploration. This blog will delve deeper into the use cases of image processing and restoration and explore ideas for projects on image processing .

Top Image Processing Project Ideas

Here are some interesting digital image processing mini projects that you must take up.

1. Monitoring Social Distancing

With COVID-19 spreading universally, it is prominent to maintain social distancing while travelling in public places. Here image processing can be a game-changer. By taking input from CCTV Cameras and analyzing one frame at a time we will achieve the task at hand.

Firstly, we use morphological operations and detection techniques to detect pedestrians in a frame. Next, we draw a bounding box surrounding each pedestrian. After which, we calculate the distance of one bounding box enclosing a pedestrian to its adjacent bounding boxes. Next, we decide a threshold for the distance between the bounding boxes and then based on their distance we categorize the pedestrians in the frame as red, yellow, or green.

The red bounding box would mean people in the frame are very close together and therefore at maximum risk. The yellow box would mean that the people are at a considerable distance and the risk is medium. The green boxes would mean people are following the norms and they are safe. Integrating this system with an alerting mechanism (Loudspeakers)could be a great way to alert the pedestrians violating the COVID-19 norms!

Such a project on image processing will not only help hone your technical skills but also stay ahead of your contemporaries in DIP project ideas.

FYI: Free nlp course !

2. Mask Detection

An interesting image processing project is mask detection.

Nowadays, wearing masks have been mandatory since the pandemic was discovered. As social distancing, mask detection is equally important to prevent any further surge in COVID cases. To detect mask. we need to first detect the human face. That can be achieved by identifying the facial landmarks such as eyes nose mouth etc. After detecting faces, we need to build an algorithm that can distinguish a face with a mask and a face without a mask.

This calls for the need for a deep learning model . Training a deep learning model on datasets comprising of both mask and non-mask images. Once the model is trained it will be able to successfully identify mask and no-mask people. Using this, we can alert pedestrians to wear masks whenever they step out of their house.

3. Lane and Curve Detection

Autonomous vehicles are the future of driving. With the aim to minimize human intervention and also the potential risk involved, many companies are spending extensively on the Research and Development of autonomous vehicle technologies. By using image segmentation for filtering and edge detection with a deep learning model we detect the presence of lane and their orientation.

Among the various image processing research topics available, this one makes for an interesting pick.

A stepwise procedure would look like this

Taking input video as frames.
Converting each frame into its corresponding grayscale image.
Reducing the prevalent noise with the help of filters.
Detecting edges using a canny edge detector.
Finding the coordinates of the road lanes.
Using deep learning to efficiently detect lanes and their orientation.

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4. drowsiness detection for drivers.

A very popular image processing project could be drowsiness detection for drivers.

The need for drowsiness detection in vehicles is necessary owing to the large number of accidents caused due to lack of consciousness amongst drivers. With a drowsiness detection system, it can alert the driver if it senses a potential loss of consciousness in the eye of the driver. By understanding and analyzing eye patterns, this system can proactively alert the driver and prevent the occurrence of accidents. This task is achieved by first locating and segmenting the eye portion from the rest of the face.

Then binarization and labelling of images are done so as to understand which images represent the occurrence of drowsiness and which don’t. Then by analyzing the blinks and their duration, the algorithm can detect drowsiness if the eyes are closed for a longer time than the time taken to blink the eye. By integrating this system with an alerting device, it could be useful in mitigating the accidents caused due to lack of consciousness.

If you are searching for ideas for image processing major projects, this one will be a perfect fit for you.

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5. license plate recognition.

One of the major projects on image processing is license plate recognition.

Yes, you heard it right, we can automate the license plate detection. Now the traffic police no longer need to manually pen down the license number of the vehicles violating the traffic rules. Thanks to the advancements in the field of image processing that such a task is possible. The steps that are required for license plate detection include- using appropriate filters to remove noise from the input image and then applying morphological operations on them.

Further, on the region of interest i.e the license plat, we apply a technique known as Optical Character Recognition (OCR)to extract text from the images. OCR is a pretrained network that is capable of detecting text from images. Using it directly will help us save the computation cost of training our algorithm by ourselves. Therefore, by following the above steps systematically, one can develop an algorithm/model to identify the license plate and the number associated with it.

Try this as one of your major image processing projects to understand the topic better .

6. Image-to-Text Conversion

Image-to-text conversion is one of the most interesting digital image processing projects using MATLAB . It is a departure from more familiar languages like Python. MATLAB’s Computer Vision Toolbox for Optical Character Recognition (OCR) makes the process accessible despite the initial unfamiliarity. You can easily leverage pre-trained language data files from OCR Language Data support in Tesseract Open Source OCR Engine.

You may also tailor the model to recognize specific character sets, such as handwritten or mathematical characters. This image-processing project acquaints you with OCR in MATLAB and presents an opportunity for customized model training, making it a versatile and engaging endeavor.

7. Background Subtraction

Background subtraction is a popular image processing project . This critical method is applied in various applications, particularly in video surveillance. Given the frame-wise nature of video analysis, this technique seamlessly integrates into the field of image processing. Its primary function involves the separation of foreground elements from the background by creating a mask.

Initiating a representation capturing background characteristics is necessary to establish the background model. Continuous updates are then made to accommodate changes, such as variations in lighting throughout different times of the day.

8. Recognition of Number Plate

The Number Plate Recognition project on image processing is ideal for beginners looking to deliver effective results for assignments or final-year projects. The development of this time-saving vehicle identification system hinges on the integration of the OCR technique.

The OCR method accurately recognizes vehicles entering designated zones. The system extracts alphanumeric vehicle numbers, ensuring precise identification. This extracted number undergoes cross-verification with the comprehensive database containing detailed vehicle information.

With MATLAB installed on personal workstations, users can seamlessly test the real-time performance of this system. Its ability to extract current data from vehicle images makes it a valuable tool for enhancing safety and security, particularly for traffic management.

9. Tracking Objects

Object tracking and surveillance applications are focal points in image processing projects . These projects facilitate effective real-time monitoring, identification, and analysis of objects or individuals within video streams, with wide-ranging implications for security, retail analytics, and traffic management.

For instance, consider a project that employs image-processing algorithms in retail environments. The system can track customer movements, analyze shopping behavior, and provide insights into popular product areas. This application is particularly beneficial for optimizing store layouts and improving customer experiences, contributing to enhanced efficiency in the retail sector. This could be one of the interesting image processing projects for final year students.

10. Barcode Detection

One of the most interesting image processing projects is barcode detection. In the domain of image processing for barcode detection, the primary objective is to guide a computer in identifying the region within an image exhibiting the maximum contours. The initial step involves converting the image into grayscale. Subsequently, various image processing techniques, such as gradient calculation, image blurring, binary thresholding, and morphology, are applied to pinpoint the area with the highest count of contours. Once identified, this region is labeled as a barcode.

Checkout: Python Project Ideas & Topics

Image Recognition using Deep Learning

Image recognition, a significant branch of computer vision, involves identifying and classifying objects or patterns within digital images. Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have revolutionized image recognition tasks, achieving state-of-the-art performance across various applications.

CNNs are designed to mimic the human visual system, consisting of multiple layers that automatically learn relevant features from images. Here is a step-by-step guide on building an image recognition project using a popular deep learning framework like TensorFlow or PyTorch:

Define the Problem: Clearly outline the objective of your image recognition project. Specify the types of objects or patterns you want the model to identify in images.
Data Collection: Obtain a labelled dataset of images relevant to your recognition task. For instance, if the project aims to classify cats and dogs, gather a diverse collection of cat and dog images.
Data Preprocessing: Resize all images to a consistent resolution and normalize pixel values to improve training efficiency. Split the dataset into training and testing sets to evaluate model performance accurately.
Model Architecture: Choose a CNN architecture suitable for image recognition. Common choices include VGG, ResNet, or MobileNet. You can also design a custom model tailored to your specific project requirements.
Model Training: Use the training dataset to train the CNN. During training, the model adjusts its internal parameters to minimize the prediction error. This process involves forward propagation, loss computation, and backpropagation to update weights.
Hyperparameter Tuning: Experiment with different hyperparameter values, such as learning rate and batch size, to optimize model performance.
Evaluation: Assess the trained model using the testing dataset. Calculate metrics like accuracy, precision, and recall to gauge how well the model generalizes to new, unseen images.
Deployment: Once satisfied with the model’s performance, deploy it to make real-time predictions on new images.
Monitor and Maintain: Continuously monitor the model’s performance in production. Consider retraining the model periodically with new data to ensure it stays relevant.

Image Enhancement and Restoration

Image enhancement and restoration techniques aim to improve the visual quality of images by correcting imperfections caused by noise, low resolution, or other factors. These techniques find applications in various fields, including medical imaging, satellite imagery, and historical photograph restoration.

Contrast Adjustment: This technique enhances the difference between light and dark regions in an image, making it visually more appealing and improving object visibility.
Denoising: Noise is an inevitable part of image acquisition and transmission. Denoising methods, such as median filtering and wavelet denoising, effectively reduce unwanted noise while preserving image details.
Super-Resolution: Super-resolution techniques reconstruct high-resolution images from their low-resolution counterparts, enhancing image clarity and sharpness.
Deblurring: This recovers details and reduces blur caused by motion, defocus, or other factors. The methods include Wiener deconvolution and blind deconvolution.
Contrast Stretching: It expands the range of pixel intensities in an image, enhancing the visibility of details by utilizing the full available dynamic range.
Wavelet Transform: Useful for both compression and enhancement, wavelet transforms break down an image into different frequency components, allowing for targeted processing.
Color Enhancement: Methods such as histogram stretching, color balancing, and saturation adjustments are applied here to improve the vibrancy and balance of colors in an image.
Histogram Equalization: It is a technique to enhance the contrast of an image by redistributing pixel intensities across a broader range, thereby improving the visibility of details.
Sparse Representation-based Approaches: It utilizes sparse representations, which aim to decompose an image into a set of basic functions, enabling efficient denoising and restoration.
Retinex Enhancement: It is based on the Retinex theory and aims to correct color and brightness variations caused by varying illumination conditions, resulting in more perceptually consistent images.

Image enhancement and restoration methods find practical use in numerous real-world projects:

In medical imaging, enhancing the visibility of anatomical structures helps physicians make accurate diagnoses and treatment decisions.
In satellite imagery, denoising can improve the quality of remote sensing data, leading to better analysis and interpretation of the Earth’s surface.
In historical photograph restoration, these techniques aid in preserving old images by reducing degradation effects and restoring missing details, allowing for a glimpse into the past with improved clarity.
Image enhancement plays a vital role in forensic investigations. Techniques such as sharpening and contrast adjustment can help reveal finer image details, aiding in evidence analysis.
Image enhancement is critical for accurate face recognition and fingerprint analysis in biometric systems, enhancing the reliability and precision of identification processes.
In restoring old or damaged artworks, image enhancement methods are employed to revive and preserve paintings or photographs’ original details, colors, and textures.
Image enhancement is widely used in remote sensing applications, such as agriculture, forestry, and environmental studies. It helps in extracting valuable information from aerial and satellite imagery.
Enhancing satellite and aerial images is crucial for GIS applications, facilitating accurate mapping, land-use planning, and environmental analysis.
Image processing techniques are employed for quality control in manufacturing processes, ensuring the detection of defects and maintaining product quality.

As technology advances, image enhancement and restoration continue to play a crucial role in enhancing visual information and enabling a wide range of applications across various industries.

Until now, we have seen 5 examples where image processing can be applied to solve the issue at hand. However, let me tell you that image processing has diversified into almost every industry almost every field is dependent on it directly or indirectly. Because it uses python as its programming language, it is convenient to use and easier to understand.

This post gives you an overview as to what is image processing and few projects associated with it. However, we do encourage you to identify more pressing problems that can be solved by leveraging the concepts of image processing.

To conclude, developing algorithms pertaining to image processing requires skill and if mastered can help you advance in your professional life at a rapid pace whilst solving real-world problems.

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Frequently Asked Questions (FAQs)

Any field in which images must be altered, edited, and evaluated relies heavily on image processing and computer vision. Remote sensing, medical imaging, autonomous vehicle navigation, and other applications rely on them. Images are typically used as the input and output to image processing operations. In contrast, computer vision usually works with input photos and produces a scene description or categorization as an output. In practice, image processing is performed as low-level computer vision operations, with the input images being filtered before high-level computer vision reasoning is performed.

The significance and requirement of digital image processing originates from two main application areas: the enhancement of input image for human interpretation and the processing of scene data for autonomous machine perception. Remote sensing, picture and data storage for transmission in corporate applications, diagnostic imaging, acoustic imaging, forensic sciences, and industrial automation are only few of the applications of digital image processing. Satellite images are useful for tracking earth resources, topographical mapping, and agricultural crop prediction, as well as weather prediction, flood and fire management.

Analogue and digital image processing are the two types of image processing methods employed. Hard copies, such as prints and photographs, can benefit from analogue image processing. When employing these visual tools, image analysts employ a variety of interpretive fundamentals. Digital image processing techniques allow for computer-assisted alteration of digital images. Pre-processing, augmentation, and presentation, as well as information extraction, are the three general processes that all sorts of data must go through when using digital techniques.

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List of Simple Image Processing Projects for ECE and CSE Students. This article also Contains Image Processing Mini Projects (which includes Digital Image Processing Projects, Medical Image Processing Projects and so on) for Final Year Engineering Students with Free PDF Downloads, Project Titles, Ideas & Topics with Abstracts & Source Code Downloads.

Image Processing Projects with Documentation and Downloads:

Python Image Processing Projects
Matlab Image Processing Projects
Java Image Processing Projects
Android Image Processing Projects
ASP.Net Image Processing Projects
Arduino Image Processing Projects
C++ Image Processing Projects
C#.Net Image Processing Projects
.Net Image Processing Projects
Embedded System Image Processing Projects
FPGA Image Processing Projects
Hadoop Image Processing Projects
IoT Image Processing Projects
C Language Image Processing Projects
VB.Net Image Processing Projects

OpenCV Image Processing Projects

PHP Image Processing Projects
Raspberry Pi Image Processing Projects
R Programming Image Processing Projects
Verilog Image Processing Projects Using
VLSI Image Processing Projects
LabVIEW Image Processing Projects
Scilab Image Processing Projects

Projects in Image Processing and Fingerprint Recognition

Python based Image Processing Projects

Real-Time Topic and Sentiment Analysis in Human-Robot Conversation Socially interactive robots, especially those designed for entertainment and companionship, must be able to hold conversations with users that feel natural and engaging for humans. Two important components of such conversations include adherence…
A GSM, WSN and Embedded Web Server Architecture The design and development of a smart monitoring and controlling system for kitchen environment in real time has been reported in this paper. The system principally monitors kitchen environment parameters such as light intensity…

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Matlab based Image Processing Projects

Image Processing Techniques using MATLAB Image processing is the field of signal processing where both the input and output signals are images. Images can be thought of as two-dimensional signals via a matrix representation, and image processing can be understood as applying standard one-dimensional signal…
A Prototype for Blood Typing Based on Image Processing This paper presents a new methodology for blood phenotyping based on the plate test and on image processing techniques to determine the occurrence of agglutination (between blood sample and reagent). A portable device for ABO-Rh blood typing and blood phenotying that…

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Simple Java Image Processing Projects

Image Processing in Java Image processing tools are being rapidly developed for different operating system platforms. These tools are usually big in size, not completely portable across different platforms and lack an ability to be efficiently fielded on the Internet…
Web Based Claims Processing System In Web Based Claims Processing System (WCPS), the employee can fill the claim form online and submit it so that the form is sent to CPD through Internet. At CPD, the form needs to be checked automatically by a program which will compute…

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Android based Image Processing Projects

Android Application for Face Recognition This report presents all the processes I use to program an android application of face recognition. At the beginning, I used the android API, after a long study of the android litterature, to make this application. Because of devices problem, I had to…
Sending Safety Video over WiMAX in Vehicle Communications This paper reports on the design of an OPNET simulation platform to test the performance of sending real-time safety vi deo over VANET (Vehicular Adhoc NETwork) using the WiMAX technology. To provide a more realistic environment for streaming real-time video…

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Image Processing Projects in ASP.Net

Web Based Claims Processing System In Web Based Claims Processing System (WCPS), the employee can fill the claim form online and submit it so that the form is sent to CPD through Internet. At CPD, the form needs to be checked automatically by a program which will compute the…
Bookshop Automation The Bookshop Automation System is to automate all operations in a bookshop. Generally it includes the Order Processing, Stock Management and Accounts Management. Before automating a bookshop we have to understand the concept of automation. In automation…

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Arduino based Image Processing Projects

Jump Trading Simulation Interface Current medical simulations for training students in the medical field involve the use of a complex computer interface to control key parameters. The Jump Trading simulation interface is an ergonomic and easy to use user interface that has been…
Accelerating Real-time Face Detection on a Raspberry Pi Telepresence Robot Effective face detection in real-time is an essential procedure for achieving autonomous motion in telepresence robots. Since the procedure demand high computation power, using it to create autonomous motion in low-cost robots is a challenge. This paper…

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C++ based Image Processing Projects

Raspberry Pi based System for Visual Object Detection and Tracking The aim of this thesis is to explore different methods for helping computers interpret the real world visually, investigate solutions to those methods offered by the open-sourced computer vision library, OpenCV, and implement some of these in a Raspberry Pi based…
A Depth of Field Algorithm for Realtime 3D Graphics in OpenGL The company where this study was formulated constructs VR applications for the medical environment. The hardware used is ordinary desktops with consumer level graphics cards and haptic devices. In medicine some operations require microscopes or cameras. In order to simulate…

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Image Processing Projects in C#.Net

Bookshop Automation The Bookshop Automation System is to automate all operations in a bookshop. Generally it includes the Order Processing, Stock Management and Accounts Management. Before automating a bookshop we have to understand the concept of automation. In…
Hospital Management System in PHP Our project Hospital Management system includes registration of patients, storing their details into the system, and also computerized billing in the pharmacy, and labs. Our software has the facility to give a unique id for every patient and stores the…

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Image Processing Projects using DotNet

Web Based Claims Processing System In Web Based Claims Processing System (WCPS), the employee can fill the claim form online and submit it so that the form is sent to CPD through Internet. At CPD, the form needs to be checked automatically by a program which will compute the amount…
Online College Portal using .Net & SQL Online College Portal (OCP) provides a simple interface for maintenance of student–faculty information. It can be used by educational institutes or colleges to maintain the records of students easily. The creation and management of accurate, update…

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Embedded Systems Image Processing Projects

Multimedia Protection using Content and Embedded Fingerprints Improved digital connectivity has made the Internet an important medium for multimedia distribution and consumption in recent years. At the same time, this increased proliferation of multimedia has raised significant challenges…
A Hybrid FPGA-based System for EEG-and EMG-based Online Movement Prediction A current trend in the development of assistive devices for rehabilitation, for example exoskeletons or active orthoses, is to utilize physiological data to enhance their functionality and usability, for example by predicting the patient’s upcoming…

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FPGA based Image Processing Projects

Image interpolation in firmware for 3D display This study investigates possibilities to perform image interpolation on an FPGA instead of on a graphics card. The images will be used for 3D display on Setred AB’s screen and an implementation in firmware will hopefully give two major advantages…

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Hadoop based Image Processing Projects

Data Streaming in Hadoop The field of distributed computing is growing and quickly becoming a natural part of large as well as smaller enterprises’ IT processes. Driving the progress is the cost effectiveness of distributed systems compared to centralized options, the physical limitations…
Understanding the Performance of Low Power Raspberry Pi Cloud for Big Data Nowadays, Internet-of-Things (IoT) devices generate data at high speed and large volume. Often the data require real-time processing to support high system responsiveness which can be supported by localised Cloud and/or Fog computing…

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IoT based Image Processing Projects

IoT Based Smart Healthcare Kit The paper presents the design and implementation of an IOT-based health monitoring system for emergency medical services which can demonstrate collection, integration, and interoperation of IoT data flexibly which can provide support to emergency…
IOT Based Smart Farming System Farming is a major input sector for economic development of any country. Livelihood of majority of population of the country like India depends on agriculture. In this project, it is proposed to develop a Smart Farming System that uses advantages of cutting edge…

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Image Processing Projects in C

Image Processing Techniques using MATLAB Image processing is the field of signal processing where both the input and output signals are images. Images can be thought of as two-dimensional signals via a matrix representation, and image processing can be understood as applying standard…
A Portal based System for Indoor Environs The purpose of this project is to document the development of the graphics part of an extremely pluggable game engine/lab environment for a course in advanced game programming. This project is one out of five, and concerns indoor, realtime computer…

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Image Processing Projects in VB.Net

Bookshop Automation The Bookshop Automation System is to automate all operations in a bookshop. Generally it includes the Order Processing, Stock Management and Accounts Management. Before automating a bookshop we have to understand the concept of automation…
Hospital Management System in PHP Our project Hospital Management system includes registration of patients, storing their details into the system, and also computerized billing in the pharmacy, and labs. Our software has the facility to give a unique id for every patient…

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Face Recognition using Image Processing for Visually Challenged In this paper the face recognition is done for the visually challenged people. Visually challenged people faces lot of problems in day to day life. Our goal is to make them lead a life which is of security and safety for their own well being. This makes…
Raspberry Pi based System for Visual Object Detection and Tracking The aim of this thesis is to explore different methods for helping computers interpret the real world visually, investigate solutions to those methods offered by the open-sourced computer vision library, OpenCV, and implement some of these in a Raspberry Pi…

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Image Processing Projects in PHP

A Prototype for Blood Typing Based on Image Processing This paper presents a new methodology for blood phenotyping based on the plate test and on image processing techniques to determine the occurrence of agglutination (between blood sample and reagent). A portable device for ABO-Rh blood typing and blood phenotying…
Hospital Management System in PHP Our project Hospital Management system includes registration of patients, storing their details into the system, and also computerized billing in the pharmacy, and labs. Our software has the facility to give a unique id for every patient and stores the details…

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Image Processing Projects using RPI

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Image Processing Projects using R

A Portal based System for Indoor Environs The purpose of this project is to document the development of the graphics part of an extremely pluggable game engine/lab environment for a course in advanced game programming. This project is one out of five, and concerns indoor, realtime…
Bluetooth Low Energy Platform with Simblee Bluetooth Low Energy provides a platform for many developers to implement low power communication for a wide range of applications. The Internet of Things (IoT) is an emerging concept that is gaining traction in the world of embedded systems…

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Verilog Image Processing Projects

Variation Aware Placement for Efficient Key Generation With the importance of data security at its peak today, many reconfigurable systems are used to provide security. This protection is often provided by FPGA-based encrypt/decrypt cores secured with secret keys. Physical unclonable functions (PUFs)…
Compressive Sensing Analog Front End Design In 180 nm CMOS Technology In order to accurately reconstruct signal waveform a signal must be sampled at least twice as fast as the bandwidth of the signal. Ultra Wideband (UWB) signals have extraordinary potential for high information transmission while a central focus of wireless…

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Image Processing based VLSI Projects

Low-Power System Design for Human-Borne Sensing Design for human-borne sensing faces a key challenge: to provide increasingly high-quality, day-by-day sensing accuracy and reporting from an energy-constrained and aggressively miniaturized computing form factor. Long-term maintenance-free operation…
Bio-inspired VLSI Systems: from Synapse to Behavior We investigate VLSI systems using biological computational principles. The elegance of biological systems throughout the structure levels provides possible solutions to many engineering challenges. Specifically, we investigate neural systems at the synaptic level…

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Image Processing Projects in LabVIEW

Portable and Automatic Biosensing Instrument for Detection of Food Borne Pathogenic Bacteria Foodborne diseases are a growing public health problem. In recent years, many rapid detection methods have been reported, but most of them are still in lab research and not practical for use in the field. In this study, a portable and automatic biosensing instrument was designed…
Customized Metal Oxide Semiconductor-based Gas Sensor Array A gas sensor array, consisting of seven Metal Oxide Semiconductor (MOS) sensors that are sensitive to a wide range of organic volatile compounds was developed to detect rotten onions during storage. These MOS sensors were enclosed in a specially designed…

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Image Processing Mini Projects using Scilab

Integrated Design and Implementation of Embedded Control Systems with Scilab Embedded systems are playing an increasingly import ant role in control engineering. Despite their popularity, embedded systems are generally subject to resource constraints and it is therefore difficult to build complex control systems on embedded platforms…

>> More Projects on Image Processing with Downloads

>> More MATLAB based Projects with Downloads

>> More Projects on Signal Processing with Downloads

BE Projects
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Image Processing Thesis
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Thesis on Image Processing

Thesis on Image Processing consists promising topic for research scholars for Interpolations a concept in image processing is used to display reasonable images in many resolutions. Image processing checks the image for unnecessary features and eliminates them in order to minimize the information. An image is processed and adjusted in terms of brightness, contrast and color.Thesis o this topic follows the sequence of paper analysis, problem formulation, algorithm derivation and finally manuscript preparation. Image can be segmented, classified and recognized in a research work.

Application of Thesis on Image Processing:

Areas that can be chosen by research scholars to base their thesis on Image Processing are as follows:

Face tracking.
Face recognition.
Document handling.
Human activity recognition.
Object recognition.
Autonomous vehicles.
Drowsiness detection.
Traffic monitoring.
Biometrics verification and identification.
Hand gesture recognition.
Target recognition.
Signature verification.

Pattern recognition:

There are two kinds of pattern recognition. They are statistical pattern recognition and structural pattern recognition only vectors are taken into account for statistical pattern recognition and they are used to perform tasks. Data in the system is transformed as discrete structure manner for structural recognition system. Students of computer science can make use of this method for graph matching and parsing.

Classification:

Items in a system are recognized by classification. Learning algorithms are a great aid in this process there are two learning algorithms namely supervised and unsupervised learning. Before hand knowledge is needed in supervised learning classification. In this method first training field is selected then signatures are evaluated and at last images are classified. Posterior knowledge is enough in unsupervised learning. It runs clustering algorithms and then the signatures are evaluated and classified.

Under sampling rates are determined by Nyquist limit this process is called as aliasing. There are two types of aliasing namely spatial and temporal aliasing. Individual images cause problems in spatial aliasing. In temporal aliasing problem occur in image sequences.

Anti aliasing :

T is an vice versa of aliasing. In this limited band signals are formed by pre filters. Blurring is done by low pass filters and trade aliasing.

Edge detection:

The differences of neighborhood pixels are detected by adding image with filters. This process is called as edge detection. It’s primary motive is to derive a line from a specific image . by using higher level computer vision algorithms needed features of images from edge is detected and retrieves edge the contrast of normal image is the edge strength.

Color image processing:

The process of extracting and identifying objects are simplified and performed effectively by this method content based image retrieval is significant application of color image processing.

Future advancement:

In our center we offer project related to satellite progress. ASTER and SAR images play an important role in the progress of satellite based projects.

Image Processing Thesis Ideas

Image Processing Thesis Ideas gives you a way to write your thesis in Image Processing. Our service is completed for over ten years, and also today, we stand as the best supporter for students in the field of image processing. Today, we also served in 120+ countries; we had our experts to support us from the topic/idea selection to the end of the final stage.

We have 150+ top experts working on Image-based thesis ideas and applications. Over the service of more than ten years, we have successfully built a 1000+ Image Processing Thesis for students from all over the globe. Our Image Thesis Ideas defines a unique convergence of knowledge and computation. Image Processing is the evergreen research field due to the rise of computation, research breakthroughs, and outstanding user-oriented design features. If you are looking for your Thesis Ideas, approach us through our online and offline services.

Processing Thesis Ideas

Image Processing Thesis Ideas startup for students and research scholars those who seeking ideas for their thesis.Our organization is one of the world’s leading companies as well as a powerhouse of technical and scientific innovation. Nowadays, image processing is working in all major fields like medical applications, military applications, agriculture-based applications, etc. Our organization is one of the world’s leading companies as well as a powerhouse of technical and scientific innovation. Our experts are also interested in image processing, computer vision, video game technologies, and computer-based simulations. In order to prepare an image processing thesis, we should be aware of the thesis structure. For this, we also concentrate more on this phase.

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Image Processing Operations and Functions

Curve fitting
Data plotting
Data visualization and analysis
Matlab Transforms
Linear and nonlinear functions
Input and output functions
Numerical functions
Signal processing functions
And also in Statistical functions

Image Collection

Medical imaging (CT, MRI, PET-CT, and also in Ultrasound, etc.)
Support images in 2D, 3D, 4D and also 5D formats
Face databases (SC face, Face-Scrub, Jeffie, Casia and also Yale)
Iris databases (Casia database, and also UTRIS cross spectral iris image data-bank)
3D datasets (Meissdor (Skull), Big Band, and also CAESAR)
Surveillance images (SPEVI, Scouter, and also in Open-visor)
Fingerprint datasets (FVC, and also in NIST datasets)
Gesture and Hand images (Yale human Grasping dataset and also Hand Net)

Image Processing Software

Vector Graphics Editing Tools: Corel Draw and also in Adobe Illustrator
Consumer Photo Tools: Windows Paint, also using Photoshop Elements, Picassa, ACDsee, Picassa and also in XV
Bitmap Editing Tools: Macormedia Fireworks and also in Adobe Photoshop
Matlab Tools: OpenCV, Mu-Pad, Simulink, Compression Tools, List of mathtools, also using Image Magick, IPL, Python Imaging Library and also in Vl-feat

We can develop your Image Processing Projects in the following areas

Digital signal processing
Data mining
Pattern recognition and also in machine learning
Statistics and probability
Partial differential equations
Visual data communication
Image sensing applications
Autonomous vehicles
Remote image sensing
Surveillance monitoring
Multimedia Retrieval

Algorithms Used in Image Processing

Contrast Enhancement

Histogram Equalization
Adaptive Histogram Equalization

Half Toning and Dithering

Ordered dithering
Error diffusion
Riemersma dithering
Floyd Steinberg dithering

Feature Detection

Canny Edge Detector
Hough Transform
Generalized Hough Transform
SIFT (Scale Invariant Feature Transform)
SURF (Speeded Up Robust Features)
And also in Marr-Hildreth Algorithm

Edge Detection

Marr-Hildreth Edge Detector
Shen-Castan Edge Detector

Image Segmentation

Region Growing
Random Walker Algorithm
Watershed transformation

Compression and Coding

Huffman coding
Chain codes
Run length encoding

Color Image Processing

Color-saturation correction
Color tint correction
True color display
RGB and YIQ color model
And also in HIS color model

List of Image Processing Thesis Ideas

Centralized collaborative sparse unmixing application also for hyper spectral images
Urban areas subpixel mapping also using Multioutput support vector regression
High resolution satellite imagery also using dictionary group learning
Content image retrieval on SAR images also using relevance feedback and fuzzy similarity
Hyperspectral image superresolution also using Transfer Learning
Deep learning also based hyperspectral image classification method
Conditional label dependence with weak labeled active learning also for multi label image classification
N-dimensional exponential signals also based on Hankel Matrix Nuclear Norm Regularized Tensor Completion
Differential evolution algorithm also for visual tracking framework

Services we offer.

Mathematical proof

Pseudo code

Conference Paper

Research Proposal

System Design

Literature Survey

Data Collection

Data Analysis

Rough Draft

Paper Collection

Code and Programs

Paper Writing

Course Work

Medical Image Processing Thesis Topics

How to choose the recent research medical image processing thesis topics? We provide the best guidance for medical imaging research work. Medical imaging is the process of inspecting the bio-medical images of human internal body organs/muscle/tissues to identify the actual patient’s health. In other words, it analyzes the medical images through intelligent image processing techniques for predicting and studying diseases.

This field offers an infinite number of imaging algorithms and methodologies to accurately assess clinical disorders. Also, it helps the physician to make effective decisions over diagnosing the disease. So, medical image processing is growing rapidly in the healthcare sector for several developments and applications.

In this page, you can find the end-to-end information on recent Medical Image Processing research with innovative ideas!!!

Latest topics in Medical Image Processing

Advance Tumor Segmentation in MR Brain Images
DL Method for Multi-Classification and Segmentation of Skin Lesion
Intelligent Diabetic Retinopathy Detection and Analysis
Improved Digestive System Disorder Recognition and Treatment

Ultimately, this technology enables us to analyze, enhance and recognize the Image in a better way to craft medical image processing thesis topics. Now, we can see about the research gaps where some research issues not solved yet or not attained efficient solutions so far.

Medical Image Processing Thesis Topics Guidance

Research Gaps in Medical Image Processing

Software and Hardware
Poor Lighting
Object and Image Quality
Real-world Environment
Minimizing Quantization
Dynamics in Image Sensor
Performance gap in Image Processing
Resampling and Reformatting Utilization
Abnormal Lens Aperture Setting
Image Digitization and Type Conversion
Printer and Document Viewer Configuration
Quality Maintenance in Image Compression

As a matter of fact, medical image processing is the extended support of image processing. This area is particularly designed for assisting medical-related fields . Majorly, it works on the below specified three primary goals for improved image visualization.

3D Generation (from 2D slice)
3D Model (viewing, reconstruction, volumetric display)
Health disorder (analysis, identification, prediction, diagnosing)
Object (search, recognition and segmentation)
Tools: X-ray, Ultrasound, MRI and more
Noise reduction
Compression
Data repositories
Enhancement (contrast and resolution) and more

In addition, we have listed the best result-generating algorithms and techniques used for common operations in medical image processing using matlab . Accordingly, it delivers an enhanced diagnostic System for solving major research issues helps to formulate medical image processing thesis topics.

What are the algorithms used in medical image processing?

Smart Region Growing
Fast Random Walker
Liquid Rescaling (Seam carving)
Content-Aware Image Resizing (CAIR)
Canny Edge Detector (CED) Algorithm
Linear based Generalised Hough Transform (Linear – GHT)
Circular Hough Transform (CHT)
Marr–Hildreth’s Edge Detection Algorithm
SIFT and SURF
Dynamic Histogram Equalization (based on nearer pixel)
Adaptive Histogram Equalization
Lucy- Richardson deconvolution Procedure
Blind deconvolution Procedure
Labeling Connected Components
Difference-Map Algorithm (by Veit Elser)
Parallel Error Diffusion
Other Dithering Algorithms (Ordered, Riemersma and Floyd–Steinberg)

Most probably, in many healthcare applications, the images are renovated for better interpretation from their original degradation features. At this time, it is necessary to assess the Image’s quantitative image quality. So, the original image is also required to evaluate the reconstructed Image. Below, we have specified few improved diagnostic approaches for medical image processing.

Current trends in Medical Image Processing

Input : Colonoscopy Video or Images
Aim : Polyp Auto-detection
Input : Dermoscopic images
Aim : Skin lesions Auto-detection and Auto-classification
Input : Cine-MRI images
Aim : Left Ventricle Auto-segmentation and LVEEF Assessment
Input : MRI images
Aim : Upper airway Auto-Volumetric-Segmentation
Input : Microscopy images
Aim : Breast Cancer Auto-Classification
Aim : Cephalometric landmarks Auto-Detection
Input : X-Ray images
Aim : 3D Surface image construction
Input : Volumetric MRI and CT images
Aim : Liver Auto-Segmentation
Aim : Brain Auto-Segmentation
Input : Optical CT images
Aim : Various Kind Fluids Auto-Segmentation and Detection
Input : CT images
Aim : Lung Auto-Segmentation
Input : Ultrasound images
Aim : Nerve Auto-Segmentation
Aim : Liver Cancer Auto-Segmentation
Aim : Brain Tumor Auto-Segmentation
Input : Chest CT images
Aim : Tumor Auto-Detection and Segmentation
Input : Microscopic images
Aim : Biological Cell Auto-Tracking
Aim : Pulmonary blood-vessels Auto-Segmentation
Input : Retinal fundus images
Aim : Diabetic Retinopathy Auto-Detection and Grading

Medical Image Processing Frameworks and Libraries

In theoretical aspects, it says everything is possible in developing image processing applications from scratch. But in the case of real-time executions of Medical Image Processing Thesis Topics , we need to employ suitable frameworks and libraries. Selection of the best development platform will make your tasks easy to implement. Hence, we have given a list of frameworks and libraries for medical image processing projects ,

Support “wrapper functtion” to incorporate .Net framework languages along with OpenCV for image processing of image
OS – iOS, Linux, Windows, Android and Mac OS
Languages – VC ++, C #, IronPython, VB, etc.
Development Tools – Unity, Visual Studio and Xamarin Studio
Offer a sophisticated learning platform for dealing with CV and huge-scale industrial applications
Simple Coding
Ultra-Fast Functions
Modeling and Optimization
BLOBs based Data Computation
Dynamic Switching from CPU to GPU
Provide open infrastructure with massive in-built predefined functions and algorithms. Also, it enables to develop real-time applications
Android version
3D Display and Search
Fundamental DS algorithms
Input and output (videos and images)
CV and DIP Techniques
Continuous integration (CI) and Optic-flow
CUDA Programming
Cross platform with embedded Test environment
Software with large number of libraries to create home-working solutions and end-point solutions for industries
Structure Restoration
Extensive Image Manipulation
Designing GUI
Geometric CV (curves, elements and points)
Set of libraries which include vector types and raster to read and manipulate geo-spatial data
Data Re-Projection
Mosaics and Shapefile Creation
Easy to obtain Raster Data
Different Format Conversion
Open-source technology with API to build the 2D or 3D medical images for segmentation process
Input Output
Cross Validation
Segmentation
Patch Analysis
Automated Evaluation
Preliminary Processing
Support JavaScript library for performing CV image processing operations( tracking, recognizing and segmenting images)
Support HTML5
Color object Tracking
Lightweight Kernel
Easy Face Detection
Intended to develop DL models for both research and industrial purposes through ML methods
Distributed Learning
Automatic Differentiation
Framework for High Performance
Support Cloudsim based System
Exploration-to-Production Transition
Enriched Libraries and Technologies
Designed to enhance the human interpretation through building and training ANN using learning approaches
Fast-Iteration
Interactive Log Viewer
Parallel Processing over Multi-Processors
Graph and Model Optimization
Designing Own Logging Service
Computation of Arithmetic Operations (tensors)
Easy-Debugging
Includes eye tracking library for real-time monitoring and tracking of visitors through webcams
Javascript Integration
Client-side Processing
Self-calibration model
Forecast the Different Views
Launched for providing well-established environment for handling image / video processing and analysis
Fractals Generation
Moving Object Detection
Extraction of Video Frames
Support Plugins through GUI
Video Filtering and Processing
Multithreading Image Processing
It is made up of differentiable practices and archives for working with basic CV and DL models
Epipolar Geometry
Color Correction
Depth Estimation
Low-level image processing
Feature and Edge recognition
Image Conversion and Filtering

Medical Image Processing Datasets

In medical image processing, we able to construct the ML model in the absence of the data. When we use more labeled data for training large datasets, it is very important for further processing in any kind of application using project dip . As are a result, it yields a better outcome than the other common approaches. For your information, we have given you a widely used image dataset in many practical implementations:

7.5 Thousand images (Visual illusions in Nature where the ANN is incorrectly classified)
Prediction of objects in 3% accuracy
Purpose: To retain the ANN stability in ambiguous images
1 million people face images (differs in physical features and bio-data)
Purpose : To minimize the unfairness in face recognition
Half a million images (using DeepFakes, Face2Face and FaceSwap techniques)
Use 1 falsification = 1000 videos sequence
Purpose: To identify unreal videos and pictures

Performance Analysis in Medical Image Processing

In the code development of the Medical Image Processing Thesis Topics, the performance evaluation process is more significant compare to others since it helps to assess the overall efficiency of the proposed System by relating it with previously used image processing technologies . Here, the following metrics measure how the Image is restored from the degradation.

Histogram Analysis
Global Consistency Error (GCE)
Probabilistic Rand Index (PRI)
Mean Structure Similarity Index Map (MSSIM)
Variation of Information (VOI)

Further, if you want to know more findings on current Medical Image Processing Thesis Topics , then communicate with us. We surely let you know the important updates on recent research activities on medical image processing.

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The 5th International Conference on Recent Trends in Image Processing and Pattern Recognition (RTIP2R) aims to attract current and/or advanced research on image processing, pattern recognition, computer vision, and machine learning. The RTIP2R will take place at the Texas A&M University—Kingsville, Texas (USA), on November 22-23, 2022, in ...
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