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Computer Vision & Pattern Recognition

Description

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.

 

RECOMMENDED COURSES
Geometric Deep Learning, Machine Learning

 

 

REFERENCES

  • R. Kimmel, Numerical geometry of images, Springer 2003
  • A. M. Bronstein, M. M. Bronstein, R. Kimmel, Numerical geometry of non-rigid shapes, Springer 2007

People

 

Bronstein M.

Course director

Monti F.

Assistant

Svoboda J.

Assistant

Additional information

Semester
Spring
Academic year
2018-2019
ECTS
6
Language
English
Education
Master of Science in Artificial Intelligence, Core course, Lecture, 2nd year

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

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

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

PhD programme of the Faculty of Informatics, Elective course, Lecture, 2nd year (4 ECTS)

PhD programme of the Faculty of Informatics, Elective course, Lecture, 1st year (4 ECTS)