Veksler, O.[Olga]
Co Author Listing * Adaptive and Move Making Auxiliary Cuts for Binary Pairwise Energies
* Animated Classic Mosaics from Video
* Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors, A
* Comparative Study of Energy Minimization Methods for Markov Random Fields, A
* Convexity Shape Prior for Binary Segmentation
* Convexity Shape Prior for Segmentation
* Dense Features for Semi-Dense Stereo Correspondence
* Disparity Component Matching for Visual Correspondence
* Dynamic Programming for Approximate Expansion Algorithm
* Efficient Graph Cut Optimization for Full CRFs with Quantized Edges
* Efficient Optimization for Hierarchically-Structured Interacting Segments (HINTS)
* Efficient parallel optimization for potts energy with hierarchical fusion
* Efficient Squared Curvature
* Extracting dense features for visual correspondence with graph cuts
* Fast Approximate Energy Minimization via Graph Cuts
* Fast dynamic programming for labeling problems with ordering constraints
* Fast Fusion Moves for Multi-Model Estimation
* Fast Solver for Truncated-Convex Priors: Quantized-Convex Split Moves, A
* Fast variable window for stereo correspondence using integral images
* GrabCut in One Cut
* Graph Cut Based Optimization for MRFs with Truncated Convex Priors
* Graph cut with ordering constraints on labels and its applications
* Graph cuts in vision and graphics: Theories and applications
* Hedgehog Shape Priors for Multi-Object Segmentation
* Image Segmentation by Nested Cuts
* Inference and Learning of Graphical Models: Theory and Applications in Computer Vision and Image Analysis
* Interactive Segmentation with Super-Labels
* Joint Optimization of Segmentation and Color Clustering
* K-convexity Shape Priors for Segmentation
* Local Submodularization for Binary Pairwise Energies
* Markov Random Fields with Efficient Approximations
* Minimizing Energies with Hierarchical Costs
* Multi-label Moves for MRFs with Truncated Convex Priors
* Multi-object Convexity Shape Prior for Segmentation
* Object Class Segmentation Using Reliable Regions
* Order-Preserving Moves for Graph-Cut-Based Optimization
* Parameter Selection for Graph Cut Based Image Segmentation
* Prostate Histopathology: Learning Tissue Component Histograms for Cancer Detection and Classification
* Recursive MDL via graph cuts: Application to segmentation
* Reducing Search Space for Stereo Correspondence with Graph Cuts
* Regularized Loss for Weakly Supervised Single Class Semantic Segmentation
* Regularized Loss With Hyperparameter Estimation for Weakly Supervised Single Class Segmentation
* Saliency Segmentation based on Learning and Graph Cut Refinement
* Semi-Dense Stereo Correspondence with Dense Features
* Semiautomatic Segmentation with Compact Shape Prior
* Simulating Classic Mosaics with Graph Cuts
* Sparse Non-local CRF
* Star Shape Prior for Graph-Cut Image Segmentation
* Stereo Correspondence by Dynamic Programming on a Tree
* Stereo Correspondence with Compact Windows via Minimum Ratio Cycle
* Stereo Matching by Compact Windows via Minimum Ratio Cycle
* Submodularization for Binary Pairwise Energies
* Superpixels and Supervoxels in an Energy Optimization Framework
* Test Time Adaptation with Regularized Loss for Weakly Supervised Salient Object Detection
* Tiered scene labeling with dynamic programming
* Variable Neighborhood Approach to Early Vision, A
* Variable Window Approach to Early Vision, A
Includes: Veksler, O.[Olga] Veksler, O.
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