V
Vladimir Kolmogorov
Researcher at Institute of Science and Technology Austria
Publications - 143
Citations - 30300
Vladimir Kolmogorov is an academic researcher from Institute of Science and Technology Austria. The author has contributed to research in topics: Cut & Submodular set function. The author has an hindex of 50, co-authored 138 publications receiving 28736 citations. Previous affiliations of Vladimir Kolmogorov include Cornell University & Microsoft.
Papers
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Journal ArticleDOI
"GrabCut": interactive foreground extraction using iterated graph cuts
TL;DR: A more powerful, iterative version of the optimisation of the graph-cut approach is developed and the power of the iterative algorithm is used to simplify substantially the user interaction needed for a given quality of result.
Journal ArticleDOI
An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision
Yuri Boykov,Vladimir Kolmogorov +1 more
TL;DR: This paper compares the running times of several standard algorithms, as well as a new algorithm that is recently developed that works several times faster than any of the other methods, making near real-time performance possible.
Book ChapterDOI
An Experimental Comparison of Min-cut/Max-flow Algorithms for Energy Minimization in Vision
Yuri Boykov,Vladimir Kolmogorov +1 more
TL;DR: The goal of this paper is to provide an experimental comparison of the efficiency of min-cut/max flow algorithms for applications in vision, comparing the running times of several standard algorithms, as well as a new algorithm that is recently developed.
Journal ArticleDOI
What energy functions can be minimized via graph cuts
Vladimir Kolmogorov,R. Zabin +1 more
TL;DR: This work gives a precise characterization of what energy functions can be minimized using graph cuts, among the energy functions that can be written as a sum of terms containing three or fewer binary variables.
Proceedings ArticleDOI
Computing visual correspondence with occlusions using graph cuts
Vladimir Kolmogorov,Ramin Zabih +1 more
TL;DR: This paper presents a new method which properly addresses occlusions, while preserving the advantages of graph cut algorithms, and gives experimental results for stereo as well as motion, which demonstrate that the method performs well both at detecting occlusion and computing disparities.