Search For:

Displaying 1-20 out of 20 total
Spatially Coherent Clustering Using Graph Cuts
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Ramin Zabih, Vladimir Kolmogorov
Issue Date:July 2004
pp. 437-444
Feature space clustering is a popular approach to image segmentation, in which a feature vector of local properties (such as intensity, texture or motion) is computed at each pixel. The feature space is then clustered, and each pixel is labeled with the cl...
 
Visual Correspondence Using Energy Minimization and Mutual Information
Found in: Computer Vision, IEEE International Conference on
By Junhwan Kim, Vladimir Kolmogorov, Ramin Zabih
Issue Date:October 2003
pp. 1033
We address visual correspondence problems without assuming that scene points have similar intensities in different views. This situation is common, usually due to non-lambertian scenes or to differences between cameras. We use maximization of mutual inform...
 
Computing Visual Correspondence with Occlusions using Graph Cuts
Found in: Computer Vision, IEEE International Conference on
By Vladimir Kolmogorov, Ramin Zabih
Issue Date:July 2001
pp. 508
Several new algorithms for visual correspondence based on graph cuts [7, 14, 17] have recently been developed. While these methods give very strong results in practice, they do not handle occlusions properly. Specifically, they treat the two input images a...
 
Partial Enumeration and Curvature Regularization
Found in: 2013 IEEE International Conference on Computer Vision (ICCV)
By Carl Olsson,Johannes Ulen,Yuri Boykov,Vladimir Kolmogorov
Issue Date:December 2013
pp. 2936-2943
Energies with high-order non-sub modular interactions have been shown to be very useful in vision due to their high modeling power. Optimization of such energies, however, is generally NP-hard. A naive approach that works for small problem instances is exh...
 
Potts Model, Parametric Maxflow and K-Submodular Functions
Found in: 2013 IEEE International Conference on Computer Vision (ICCV)
By Igor Gridchyn,Vladimir Kolmogorov
Issue Date:December 2013
pp. 2320-2327
The problem of minimizing the Potts energy function frequently occurs in computer vision applications. One way to tackle this NP-hard problem was proposed by Kovtun [19, 20]. It identifies a part of an optimal solution by running k maxflow computations, wh...
 
Graph cut based image segmentation with connectivity priors
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Sara Vicente, Vladimir Kolmogorov, Carsten Rother
Issue Date:June 2008
pp. 1-8
Graph cut is a popular technique for interactive image segmentation. However, it has certain shortcomings. In particular, graph cut has problems with segmenting thin elongated objects due to the “shrinking bias”. To overcome this problem, we propose to imp...
 
A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Richard Szeliski, Ramin Zabih, Daniel Scharstein, Olga Veksler, Vladimir Kolmogorov, Aseem Agarwala, Marshall Tappen, Carsten Rother
Issue Date:June 2008
pp. 1068-1080
Among the most exciting advances in early vision has been the development of efficient energy minimization algorithms for pixel-labeling tasks such as depth or texture computation. It has been known for decades that such problems can be elegantly expressed...
 
Applications of parametric maxflow in computer vision
Found in: Computer Vision, IEEE International Conference on
By Vladimir Kolmogorov, Yuri Boykov, Carsten Rother
Issue Date:October 2007
pp. 1-8
The maximum flow algorithm for minimizing energy functions of binary variables has become a standard tool in computer vision. In many cases, unary costs of the energy depend linearly on parameter ¿. In this paper we study vision applications for which it i...
 
Minimizing Nonsubmodular Functions with Graph Cuts-A Review
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Vladimir Kolmogorov, Carsten Rother
Issue Date:July 2007
pp. 1274-1279
Optimization techniques based on graph cuts have become a standard tool for many vision applications. These techniques allow to minimize efficiently certain energy functions corresponding to pairwise Markov Random Fields (MRFs). Currently, there is an acce...
 
Optimizing Binary MRFs via Extended Roof Duality
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Carsten Rother, Vladimir Kolmogorov, Victor Lempitsky, Martin Szummer
Issue Date:June 2007
pp. 1-8
Many computer vision applications rely on the efficient optimization of challenging, so-called non-submodular, binary pairwise MRFs. A promising graph cut based approach for optimizing such MRFs known as
 
Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Carsten Rother, Tom Minka, Andrew Blake, Vladimir Kolmogorov
Issue Date:June 2006
pp. 993-1000
We introduce the term cosegmentation which denotes the task of segmenting simultaneously the common parts of an image pair. A generative model for cosegmentation is presented. Inference in the model leads to minimizing an energy with an MRF term encoding s...
 
What Metrics Can Be Approximated by Geo-Cuts, Or Global Optimization of Length/Area and Flux
Found in: Computer Vision, IEEE International Conference on
By Vladimir Kolmogorov, Yuri Boykov
Issue Date:October 2005
pp. 564-571
<p>In [3] we showed that graph cuts can find hyper-surfaces of globally minimal length (or area) under any Riemannian metric. Here we show that graph cuts on directed regular grids can approximate a significantly more general class of continuous non-...
 
Digital Tapestry
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Carsten Rother, Sanjiv Kumar, Vladimir Kolmogorov, Andrew Blake
Issue Date:June 2005
pp. 589-596
This paper addresses the novel problem of automatically synthesizing an output image from a large collection of different input images. The synthesized image, called a digital tapestry, can be viewed as a visual summary or a virtual ?thumbnail? of all the ...
 
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Yuri Boykov, Vladimir Kolmogorov
Issue Date:September 2004
pp. 1124-1137
After [15], [31], [19], [8], [25], [5], minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/m...
 
What Energy Functions Can Be Minimizedvia Graph Cuts?
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Vladimir Kolmogorov, Ramin Zabih
Issue Date:January 2004
pp. 147-159
<p><b>Abstract</b>—In the last few years, several new algorithms based on graph cuts have been developed to solve energy minimization problems in computer vision. Each of these techniques constructs a graph such that the minimum cut on th...
 
Computing Geodesics and Minimal Surfaces via Graph Cuts
Found in: Computer Vision, IEEE International Conference on
By Yuri Boykov, Vladimir Kolmogorov
Issue Date:October 2003
pp. 26
Geodesic active contours and graph cuts are two standard image segmentation techniques. We introduce a new segmentation method combining some of their benefits. Our main intuition is that any cut on a graph embedded in some continuous space can be interpre...
 
A new look at reweighted message passing
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Vladimir Kolmogorov
Issue Date:February 2015
pp. 1
We propose a new family of message passing techniques for MAP estimation in graphical models which we call Sequential Reweighted Message Passing (SRMP). Special cases include well-known techniques such as Min-Sum Diffusion (MSD) and a faster Sequential Tre...
 
The complexity of conservative valued CSPs
Found in: Journal of the ACM (JACM)
By Stanislav Živný, Vladimir Kolmogorov
Issue Date:April 2013
pp. 1-38
We study the complexity of valued constraint satisfaction problems (VCSPs) parametrized by a constraint language, a fixed set of cost functions over a finite domain. An instance of the problem is specified by a sum of cost functions from the language and t...
     
On partial optimality in multi-label MRFs
Found in: Proceedings of the 25th international conference on Machine learning (ICML '08)
By Alexander Shekhovtsov, Carsten Rother, Philip Torr, Pushmeet Kohli, Vladimir Kolmogorov
Issue Date:July 2008
pp. 480-487
We consider the problem of optimizing multilabel MRFs, which is in general NP-hard and ubiquitous in low-level computer vision. One approach for its solution is to formulate it as an integer linear programming and relax the integrality constraints. The app...
     
"GrabCut": interactive foreground extraction using iterated graph cuts
Found in: ACM Transactions on Graphics (TOG)
By Andrew Blake, Carsten Rother, Vladimir Kolmogorov
Issue Date:August 2004
pp. 309-314
The problem of efficient, interactive foreground/background segmentation in still images is of great practical importance in image editing. Classical image segmentation tools use either texture (colour) information, e.g. Magic Wand, or edge (contrast) info...
     
 1