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
Progressive probabilistic Hough transform for line detection
TL;DR: The algorithm is ideally suited for real-time applications with a fixed amount of available processing time, since voting and line detection is interleaved and the most salient features are likely to be detected first.
Book ChapterDOI
BOP: Benchmark for 6D Object Pose Estimation
Tomas Hodan,Frank Michel,Eric Brachmann,Wadim Kehl,Anders Buch,Dirk Kraft,Bertram Drost,Joel Vidal,Stephan Ihrke,Xenophon Zabulis,Caner Sahin,Fabian Manhardt,Federico Tombari,Tae-Kyun Kim,Jiri Matas,Carsten Rother +15 more
TL;DR: In this article, the authors propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image, which consists of a texture-mapped 3D object model or images of the object in known 6D poses.
Proceedings ArticleDOI
D3S – A Discriminative Single Shot Segmentation Tracker
TL;DR: Without per-dataset finetuning and trained only for segmentation as the primary output, D3S outperforms all trackers on VOT2016, VOT2018 and GOT-10k benchmarks and performs close to the state-of-the-artTrackers on the TrackingNet.
Proceedings ArticleDOI
MAGSAC: Marginalizing Sample Consensus
TL;DR: In this article, a method called sigmaconsensus is proposed to eliminate the need for a user-defined inlier-outlier threshold in RANSAC, which is marginalized over a range of noise scales.
Proceedings ArticleDOI
Randomized RANSAC with T(d, d) test.
Jiri Matas,Ondrej Chum +1 more
TL;DR: A new randomized (hypothesis evaluation) version of the RANSAC algorithm, R-RANSAC, is introduced and a mathematically tractable class of statistical preverification tests for test samples is introduced that derives an approximate relation for the optimal setting of its single parameter.