Logo ČVUT
CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2019/2020

Reading group in Pattern Recognition and Computer Vision

Login to KOS for course enrollment Display time-table
Code Completion Credits Range Language
XP33RCV ZK 4 2P+2S
Lecturer:
Ondřej Chum (guarantor), Georgios Tolias
Tutor:
Ondřej Chum (guarantor), Georgios Tolias
Supervisor:
Department of Cybernetics
Synopsis:

The course deals with fundamental results from computer vision, pattern recognition, mathematics of uncertainty, and knowledge-based systems. Emphasis is put on analysis of images and videos, the use of semantical information, and pattern recognition. The course treats selected key classical results, as well as lastest areas of research, especially those which substantially influence the development in the subject field. Education is performed in the form of a reading group.

Requirements:
Syllabus of lectures:
Syllabus of tutorials:
Study Objective:
Study materials:

[1] CHANDRA, S. a I. KOKKINOS. Fast, Exact and Multi-scale Inference for Semantic Image Segmentation with Deep Gaussian CRFs.In LEIBE, B., J. MATAS, N. SEBE a M. WELLING, ed. Computer Vision – ECCV 2016. Cham: Springer International Publishing, 2016, pp. 402-418. Lecture Notes in Computer Science. DOI: 10.1007/978-3-319-46478-7_25. ISBN 978-3-319-46477-0.

[2] ZAHEER, M., S. KOTTUR, S. RAVANBAKHSH, B. PÓCZOS, R. R. SALAKHUTDINOV a A. J. SMOLA.

Deep Sets. NIPS 2017. 3394-3404.

[3] BABENKO, A. a V. LEMPITSKY. The Inverted Multi-Index. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2015, 37(6), pp. 1247-1260. DOI: 10.1109/TPAMI.2014.2361319. ISSN 0162-8828.

[4] BESSE, F., C. ROTHER, A. FITZGIBBON a J. KAUTZ. PMBP: PatchMatch Belief Propagation for Correspondence Field Estimation. International Journal of Computer Vision. 2014, 110(1), pp. 2-13. DOI: 10.1007/s11263-013-0653-9. ISSN 0920-5691.

Note:
Time-table for winter semester 2019/2020:
Time-table is not available yet
Time-table for summer semester 2019/2020:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2020-01-28
For updated information see http://bilakniha.cvut.cz/en/predmet6019906.html