• Publications
  • Influence
Fast Approximate Energy Minimization via Graph Cuts
TLDR
This work presents two algorithms based on graph cuts that efficiently find a local minimum with respect to two types of large moves, namely expansion moves and swap moves that allow important cases of discontinuity preserving energies. Expand
Fast approximate energy minimization via graph cuts
TLDR
This paper proposes two algorithms that use graph cuts to compute a local minimum even when very large moves are allowed, and generates a labeling such that there is no expansion move that decreases the energy. Expand
A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors
TLDR
A set of energy minimization benchmarks are described and used to compare the solution quality and runtime of several common energy minimizations algorithms and a general-purpose software interface is provided that allows vision researchers to easily switch between optimization methods. Expand
Markov random fields with efficient approximations
TLDR
This paper shows that the maximum a posteriori estimate of such an MRF can be obtained by solving a multiway minimum cut problem on a graph, and develops efficient algorithms for computing good approximations to the minimum multiway, cut. Expand
Superpixels and Supervoxels in an Energy Optimization Framework
TLDR
This work forms the superpixel partitioning problem in an energy minimization framework, and explores variations of the basic energy, which allow a trade-off between a less regular tessellation but more accurate boundaries or better efficiency. Expand
Star Shape Prior for Graph-Cut Image Segmentation
  • Olga Veksler
  • Mathematics, Computer Science
  • ECCV
  • 12 October 2008
TLDR
This paper shows how to implement a star shape prior into graph cut segmentation, a generic shape prior that applies to a wide class of objects, in particular to convex objects, and shows that in many cases, it can achieve an accurate object segmentation with only a single pixel, the center of the object, provided by the user, which is rarely possible with standard graph cut interactive segmentation. Expand
Efficient graph-based energy minimization methods in computer vision
Energy minimization is an elegant approach to computer vision. Vision problems usually have many solutions due to uncertainties in the imaging process and ambiguities in visual interpretation. TheExpand
A Comparative Study of Energy Minimization Methods for Markov Random Fields
TLDR
A set of energy minimization benchmarks, which are used to compare the solution quality and running time of several common energy minimizations algorithms, as well as a general-purpose software interface that allows vision researchers to easily switch between optimization methods with minimal overhead. Expand
GrabCut in One Cut
TLDR
This work proposes a new energy term explicitly measuring L1 distance between the object and background appearance models that can be globally maximized in one graph cut and shows that in many applications this simple term makes NP-hard segmentation functionals unnecessary. Expand
Fast variable window for stereo correspondence using integral images
  • Olga Veksler
  • Mathematics, Computer Science
  • IEEE Computer Society Conference on Computer…
  • 18 June 2003
TLDR
A fast and accurate variable window approach based on the integral image technique, which allows computation of the window cost over any rectangular window in constant time, regardless of window size. Expand
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