J
Jiri Matas
Researcher at Czech Technical University in Prague
Publications - 359
Citations - 50878
Jiri Matas is an academic researcher from Czech Technical University in Prague. The author has contributed to research in topics: RANSAC & Video tracking. The author has an hindex of 78, co-authored 345 publications receiving 44739 citations. Previous affiliations of Jiri Matas include University of Surrey & IEEE Computer Society.
Papers
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Proceedings ArticleDOI
Training sequential on-line boosting classifier for visual tracking
TL;DR: This paper shows how the on-line boosting can be combined with Waldpsilas sequential decision theory to solve both of the problems of classifier evaluation speed optimization and automatic classifier complexity estimation.
The VOT2013 challenge: overview and additional results
Matej Kristan,Roman Pflugfelder,Ales Leonardis,Jiri Matas,Fatih Porikli,Luka Cehovin,Georg Nebehay,Gustavo Fernandez,Tomas Vojir +8 more
TL;DR: An overview of the VOT2013 challenge is provided, its main results are pointed out, and the additional previously unpublished experiments and results are documented.
Book ChapterDOI
Local Affine Frames for Image Retrieval
TL;DR: A novel approach to content-based image retrieval that supports recognition of objects under a very wide range of viewing and illumination conditions and is robust to occlusion and background clutter is presented.
Book ChapterDOI
3D geometry from uncalibrated images
George Kamberov,Gerda Kamberova,Ondrej Chum,Š. Obdržálek,Daniel Martinec,Jana Kostková,Tomas Pajdla,Jiri Matas,Radim Sara +8 more
TL;DR: The contributions in the paper are the presentation of the system as a whole, the estimation of the local scale for various scene components in the orientation-topology module, the procedure for orienting the cloud components, and the method for dealing with points of contact.
Proceedings ArticleDOI
Deep structured-output regression learning for computational color constancy
TL;DR: Experiments demonstrate that the structured-output regression on the values of the fully-connected layers of a convolutional neural network achieves competitive performance, outperforming the state of the art on the SFU indoor benchmark.