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 :
b. At the end of this course, the student will be able to:
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;
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
- 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 until September 13.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.
Due to the COVID-19 crisis, the information in this section is particularly likely to change until September 13.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.