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 definition: pixels, resolutions and color systems
- Image representation: from Fourier to wavelets.
- Sparsity principles: from orthonormal bases to redundant systems
- Tools for sparse decomposition: Matching Pursuit, greedy methods and Basis pursuit.
- Sparsity and applications: denoising, deconvolution, compressed sensing, computational imaging.
- Image perception, human visual system and application to watermarking.
- Image and Video compression: JPEG, MPEG, and sparse approximation coding.
- Basic tools of image analysis: filtering, thresholding, mathematical morphology.
- Image Segmentation: clustering, watershed, graph cuts and Markov random fields.
- Visual detection and recognition: point descriptors, image features, and classifiers.
- Visual object tracking: Template matching, particle filters, graph-based formalisms
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.
Besides the lectures, a learning procedure "by problems" is implemented: a practical challenge is addressed by group of 2 or 3 students, based on a software platform for image processing. The envisioned solution and its implementation are carefully validated and evaluated, before a final oral and written presentation.
- 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.
- A project (realized by a team of 2 or 3 students): The objective is to solve an actual problem in image processing and/or computer vision. Each group first prepares a brief midterm presentation (not rated); the objective is to evaluate the group progression in the project realization and to provide them advices on the selected approach and methodologies. The final project rating is based on a written report and on a final oral presentation made in December.
- 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 Matlab.
Les documents du cours sont disponibles sur Moodle
Lectures conseillées :
Durant l'année, l'étudiant doit lire 3 articles sélectionnés dans une liste de 40 articles distribués sur le site Moodle du cours.