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(May 27) Matrix Algebra and Sparse Representation for Computer Vision


Matrix Algebra and Sparse Representation for Computer Vision


16:00, Wednesday, May 27, 2015


Prof. Yasuyuki Matsushita (Osaka University)


N1, Room 111


Linear systems are everywhere in science and engineering. Computer vision as well relies on a lot of linear algebraic operations. This talk will cover some basic concepts of linear algebra and sparse representation that often appear in computer vision and signal processing by taking examples from norm minimization. After hearing this talk, I believe you will be able to comprehensively formulate your problem in an energy minimization framework and identify the solution strategy.


Yasuyuki Matsushita received his B.S., M.S. and Ph.D. degrees in EECS from the University of Tokyo in 1998, 2000, and 2003, respectively. From April 2003 to March 2015, he was with Visual Computing group at Microsoft Research Asia. In April 2015, he joined Osaka University as a professor. His research area includes computer vision, machine learning and optimization. He is on the editorial board of IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), International Journal of Computer Vision (IJCV), IPSJ Journal of Computer Vision and Applications (CVA), The Visual Computer Journal, and Encyclopedia of Computer Vision. He served/is serving as a Program Co-Chair of PSIVT 2010, 3DIMPVT 2011, ACCV 2012, ICCV 2017, and a General Co-Chair for ACCV 2014. He is a senior member of IEEE.