# Image processing, analysis and synthesis

Module coordinator: Adrien BOUSSEAU

Lecturers: Adrien BOUSSEAU, George DRETTAKIS, Frédéric PRECIOSO, Rachid DERICHE, Théo PAPADOPOULO

### Description:

- 6 ECTS

This course is divided in 4 parts that cover the basic concepts of computational imagery. Each part is composed of a set of lectures and lab exercises.

- Machine Learning for Computer Vision (Frederic Precioso, 12h – 6h lecture and 6h lab): basic image transformations (histogram equalization, mean-shift filtering and tracking), classifiers and boosting, face detection, neural networks.

- Advanced Computational Image Processing (Rachid Deriche, 12h – 6h lecture and 6h exercises): linear & non-linear image filtering, edge detection and image features, snakes & geodesic active contours via level-set, image enhancements & restoration, segmentation & application to medical imaging.

- 3D Computer Vision (Théodore Papadopoulo, 12h): camera model, calibration, stereo vision, mosaicing, 3D reconstruction, elements of 3D shape representation.

- Computer Graphics (Adrien Bousseau, George Drettakis, 18h – 10h lecture and 8h lab): raytracing and rasterization, lighting, texturing, shadows, image-based rendering, OpenGL and GPU programming.

### Grading

The evaluation will be based on three grades:

- Short exams (10-20 mins) and lab exercises. Short exams in Advanced Computational Image Processing and in 3D Computer Vision, lab exercises in Computer Graphics and in Machine Learning for Computer Vision.
- Project. One project in Advanced Computational Image Processing or Computer Graphics. Students with express their preferences over a list of projects and we will assign one project per student.
- Long exam. One long exam that covers 3D Computer Vision and Machine Learning for Computer Vision.

### Prerequisites

Basics of programming (C++) and mathematics (linear algebra, geometry)

### Bibliography:

- Fundamentals of Computer Graphics, Peter Shirley, Michael Ashikhmin, Steve Marschner
- Physically Based Rendering, from Theory to Implementation, Matt Pharr, Greg Humphreys
- Image Processing and Analysis : Variational, PDE, Wavelets and Stochastic Methods, T. Chan & J. Shen - Siam 2005
- Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations, G. Aubert and P. Kornprobst - Text in Applied Mathematics vol 147, 2002, Springer Verlag
- Geometric Partial Differential Equations and Image Processing, G. Sapiro - Cambridge University Press, January 2001
- Level Set Methods and Fast Marching Methods Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science, J. Sethian - Cambridge University Press, 1999
- 3D Computer Vision - A Geometric ViewPoint, O.D.Faugeras
- Multiple View Geometry in Computer Vision, R. Hartley & A. Zisserman - Cambridge University Press, 2004
- The Geometry of multiple images, O.D. Faugeras - Quang-Tuan Luong & T. Papadopoulo - The MIT Press - 2001