At the end of this learning unit, the student is able to :
With respect to the AA referring system defined for the Master in Electrical Engineering, the course contributes to the develoopment, mastery and assessment of the following skills :
1. Handle techniques of representation and approximation of images in order to extract their meaningful components with respect to a particular application, for example, in the fields of data transmission or interpretation;
2. Apply linear and non-linear filtering operations (e.g., morphological) to isolate certain frequency components or to cancel particular noises;
3. Detect structures of interest in an image, such as contours, key features, etc..
4. Segment an image into regions of homogeneous characteristics, targeting a semantic interpretation of the image content;
5. Restore images corrupted a noise or a blurring;
6. Understand the basic principles of inverse problem solving in imaging and in compressed sensing;
7. Manage image databases using detection tools or classification;
8. Detect and track one or more object(s) of interest in video streams, in biomedical applications or for 3-D scene interpretation;
9. Compress image signals considering their visual perception and their accessability in the compressed signal representation;
10. Provide a solution to complex problems involving image processing, such as quality control, visiosurveillance, multimodal human-machine interfaces, and image compression.
- Image representation: Pixels, Fourier and Multiscale Transforms.
- The wavelet transform.
- The sparsity principle and applications: from orthonormal bases to redundant systems.
- Human visual system and salient image features.
- Image classification and deep learning introduction.
- Basic tools of image analysis: mathematical morphology and relatives.
- Image segmentation, (spectral) clustering, watershed and level sets
- An introduction to computational imaging
- Detection-based (multi-) object tracking: detect-before-track
- Recursive visual object tracking: track-before-detect
- Principles of stereo vision
- From entropy coding to image compression
- Video compression, and sparse approximation coding
Due to the COVID-19 crisis, the information in this section is particularly likely to change.The course is organized around a series of lectures, each dealing with a specific problem commonly encountered in the field of image processing. Each lesson introduces a selection of the main solutions found in the literature and/or the industry to solve the problem of interest, and a list of references is provided for each covered topic.
To complement the lectures, the student is also asked to read and criticize a number of scientific publications. The goal is to allow him/her to deal with a subject in depth, but also and especially to draw his/her attention to the way a scientific paper is built.
In addition to the theoretical classes, numerical exercise sessions under Python are organized in a computer room. Students are asked to program different algorithms associated with a consistent sub-selection of the techniques taught. They use existing Python libraries for this purpose. Learning is provided by problem solving, based on real or synthetic images/signals, sometimes associated with external databases.
The course is given in the classroom exclusively. However, in the context of health measures related to Covid-19, some lectures could be organized on a distance (or hybrid) basis, according to the terms and schedule displayed on the moodle page of the course.
Due to the COVID-19 crisis, the information in this section is particularly likely to change.The evaluation includes three components :
- An oral examination: Scheduled in January, this test evaluates individually the students on their understanding of the concepts and methods taught during the theoretical courses.
- An evaluation of the Python numerical exercises: students are evaluated on a computer (in session or out of session) based on problems similar to those presented during the year.
- A critical analysis of 3 scientific papers in the field: This helps the student to develop his ability to analyze the advantages and the weaknesses of a scientific work, considering both its content and its general structure. Each student provides a report (1 page max per article) by December.
Transparents, articles tutoriaux et parties de code Python.
Les documents du cours sont disponibles sur Moodle
Lectures conseillées :
Slides, tutorials and parts of Python code.
Course documents are available on Moodle
During the year, each student must read 3 articles selected from a list of articles distributed on the Moodle site of the course.
- on the critical analysis of 3 scientific articles (as announced above in a context of standard evaluation);
- by means of continuous evaluations associated with practical work, face-to-face if the health situation allows it, or remotely if the health situation requires it;
- individually in oral examination, face-to-face if the health situation allows it, or remotely if the health situation requires it.
The final mark (between the continuous evaluation, the 3 article reviews, and the oral examination) will follow the same weighting as announced in standard evaluation mode (in absence of pandemic).