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:

1. 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.
2. 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.
3. 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