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Computer Vision & Pattern Recognition


The purpose of the course is to introduce basic problems and notions in image processing, computer vision, and patter recognition though a common geometric framework and present some classical, industry-standard and state-of-the-art methods through this framework. The course uses tools from differential geometry, calculus of variations, and numerical optimization to address problems such as image recovery (denoising, impainting, deconvolution), filtering (adaptive diffusion, bilateral and non-local means filters), 3D structure reconstruction (shape from shading, stereo, photometric stereo); and rigid and non-rigid similarity and correspondence (iterative closest point methods, multidimensional scaling, Gromov-Hausdorff distance). The emphasis is made on both formulating a rigorous mathematical model of the problem and developing an efficient numerical method for its solution, with hands-on programming exercises that solve real-world problems.




  • R. Kimmel, Numerical Geometry of Images, Springer, 2004. 
  • A. M. Bronstein, M. M. Bronstein, R. Kimmel, Numerical geometry of non-rigid shapes, Springer, 2008.



The course is not offered in the academic year 2017/18



Bronstein M.

Course director

Additional information

Academic year
Master of Science in Artificial Intelligence, Core course, Lecture, 1st and 2nd year

Master of Science in Computational Science, Elective course, Lecture, 1st and 2nd year

Master of Science in Informatics, Elective course, Lecture, 1st and 2nd year

PhD programme of the Faculty of Informatics, Elective course, Lecture, 1st, 2nd and 3rd year