Y
Yuri Boykov
Researcher at University of Waterloo
Publications - 124
Citations - 32510
Yuri Boykov is an academic researcher from University of Waterloo. The author has contributed to research in topics: Image segmentation & Cut. The author has an hindex of 44, co-authored 124 publications receiving 29588 citations. Previous affiliations of Yuri Boykov include Carnegie Mellon University & University of Western Ontario.
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On Regularized Losses for Weakly-supervised CNN Segmentation
Meng Tang,Federico Perazzi,Abdelaziz Djelouah,Ismail Ben Ayed,Christopher Schroers,Yuri Boykov +5 more
TL;DR: This approach simplifies weakly-supervised training by avoiding extra MRF/CRF inference steps or layers explicitly generating full masks, while improving both the quality and efficiency of training.
Proceedings ArticleDOI
Active Graph Cuts
O. Juan,Yuri Boykov +1 more
TL;DR: The new Active Cuts method can effectively use a good approximate solution that is often available in dynamic, hierarchical, and multi-label optimization problems in vision, and works faster than the state-of-the-art max-flow methods even if initial cut is far from the optimal one.
Journal ArticleDOI
Semiautomatic segmentation with compact shape prior
TL;DR: This work presents an interactive segmentation algorithm that can segment an object of interest from its background with minimum guidance from the user, who just has to select a single seed pixel inside the object ofinterest.
Book ChapterDOI
An integral solution to surface evolution PDEs via geo-cuts
TL;DR: This work formulate an optimization problem directly based on an integral characterization of gradient flow as an infinitesimal move of the (whole) surface giving the largest energy decrease among all moves of equal size and shows that this problem can be efficiently solved using recent advances in algorithms for global hypersurface optimization.
Proceedings ArticleDOI
Fast approximate energy minimization with label costs
TL;DR: In this article, the authors extend α-expansion so that it can simultaneously optimize label costs, which can penalize a solution based on the set of labels that appear in it, and prove optimality bounds for their algorithm.