Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Foundations and Trends® in Computer Graphics and Vision
Dynamic Graph Cuts for Efficient Inference in Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Layered Motion Segmentations of Video
International Journal of Computer Vision
Simultaneous Segmentation and Pose Estimation of Humans Using Dynamic Graph Cuts
International Journal of Computer Vision
Topology cuts: A novel min-cut/max-flow algorithm for topology preserving segmentation in N-D images
Computer Vision and Image Understanding
Computer Vision and Image Understanding
Multi-Class Segmentation with Relative Location Prior
International Journal of Computer Vision
Fast Generalized Belief Propagation for MAP Estimation on 2D and 3D Grid-Like Markov Random Fields
Proceedings of the 30th DAGM symposium on Pattern Recognition
Semiautomatic segmentation with compact shape prior
Image and Vision Computing
Learning to Combine Bottom-Up and Top-Down Segmentation
International Journal of Computer Vision
International Journal of Computer Vision
Contour Grouping with Partial Shape Similarity
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Robust Higher Order Potentials for Enforcing Label Consistency
International Journal of Computer Vision
Contour Grouping Based on Contour-Skeleton Duality
International Journal of Computer Vision
Latent mixture vocabularies for object categorization and segmentation
Image and Vision Computing
Shape Based Detection and Top-Down Delineation Using Image Segments
International Journal of Computer Vision
TSVM-HMM: Transductive SVM based hidden Markov model for automatic image annotation
Expert Systems with Applications: An International Journal
Shape-Based Object Localization for Descriptive Classification
International Journal of Computer Vision
Foreground Segmentation via Segments Tracking
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Interactive Image Segmentation Based on Hierarchical Graph-Cut Optimization with Generic Shape Prior
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Patch Growing: Object segmentation using spatial coherence of local patches
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POSIT: Part-based object segmentation without intensive training
Pattern Recognition
Patch Growing: Object segmentation using spatial coherence of local patches
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OPTIMOL: Automatic Online Picture Collection via Incremental Model Learning
International Journal of Computer Vision
International Journal of Computer Vision
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Bayesian inference for layer representation with mixed Markov random field
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Better foreground segmentation for 3D face reconstruction using graph cuts
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
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Robust bilayer video segmentation by adaptive propagation of global shape and local appearance
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ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
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Non-parametric iterative model constraint graph min-cut for automatic kidney segmentation
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Computer Vision and Image Understanding
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Skeleton Search: Category-Specific Object Recognition and Segmentation Using a Skeletal Shape Model
International Journal of Computer Vision
Image segmentation with a shape prior based on simplified skeleton
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
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International Journal of Computer Vision
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ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Segmenting highly articulated video objects with weak-prior random forests
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Fast memory-efficient generalized belief propagation
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
An energy minimization approach to the data driven editing of presegmented images/volumes
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
International Journal of Computer Vision
Located hidden random fields: learning discriminative parts for object detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Graph-Cut Energy Minimization for Object Extraction in MRCP Medical Images
Journal of Medical Systems
POSECUT: simultaneous segmentation and 3D pose estimation of humans using dynamic graph-cuts
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Single-Histogram class models for image segmentation
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Learning class-specific edges for object detection and segmentation
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Learning segmentation of documents with complex scripts
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Using strong shape priors for stereo
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Segmentation of objects in a detection window by Nonparametric Inhomogeneous CRFs
Computer Vision and Image Understanding
Object Recognition by Sequential Figure-Ground Ranking
International Journal of Computer Vision
Segmentation over detection by coupled global and local sparse representations
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Adaptive shape prior in graph cut image segmentation
Pattern Recognition
Computer Vision and Image Understanding
Efficient segmentation of leaves in semi-controlled conditions
Machine Vision and Applications
The Shape Boltzmann Machine: A Strong Model of Object Shape
International Journal of Computer Vision
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In this paper we present a principled Bayesian method for detecting and segmenting instances of a particular object category within an image, providing a coherent methodology for combining top down and bottom up cues. The work draws together two powerful formulations: pictorial structures (PS) and Markov random fields (MRFs) both of which haveefficient algorithms for their solution. The resulting combination, which we call the Object Category Specific MRF, suggests a solution to the problem that has long dogged MRFs namely that they provide a poor prior for specific shapes. In contrast, our model provides a prior that is global across the image plane using the PS. We develop an efficient method, OBJ CUT, to obtain segmentations using this model. Novel aspects of this method include an efficient algorithm for sampling the PS model, and the observation that the expected log likelihood of the model can be increased by a single graph cut. Results are presented on two object categories, cows and horses. We compare our methods to the state of the art in object category specific image segmentation and demonstrate significant improvements.