Graph Cuts and Efficient N-D Image Segmentation
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Summary
This application epitomizes the best features of combinatorial graph cuts methods in vision: global optima, practical efficiency, numerical robustness, ability to fuse a wide range of visual cues and constraints, unrestricted topological properties of segments, and applicability to N-D problems.
- Type
- article
- Published
- 2006-11-01
- Cited by
- 2,238
- References
- 86
- Access
- Open access
- OpenAlex
- https://openalex.org/W2119300483
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:3027134
Keywords
Segmentation, Cut, Image segmentation, Computer science, Artificial intelligence
References
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- Efficient Graph-Based Image Segmentation
- "Ratio regions": a technique for image segmentation
- Hierarchical Segmentation Satisfying Constraints
- User-Steered Image Segmentation Paradigms: Live Wire and Live Lane
- HETEROGENEOUS AGENT SYSTEMS, by V.S. Subrahmanian, Piero Bonatti, Jürgen Dix, Thomas Etier, Sarit Kraus, Fatma Ozcan and Robert Ross, MIT Press, Cambridge, Mass., 2000, xiv+580pp., ISBN 0-262-19436-8 (Hardback, £39.95).
- Fast Marching Methods
- Level Set Methods and Fast Marching Methods
- Graphcut textures: image and video synthesis using graph cuts
- On active contour models and balloons
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- Robust Image Segmentation Applied to Magnetic Resonance and Ultrasound Images of the Prostate. (Segmentation d'images robuste appliqué à l'imagerie par résonance magnétique et l'échographie de la prostate)
- Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets
- Spine Image Fusion Via Graph Cuts
- Massively Multithreaded Maxflow for Image Segmentation on the Cray XMT-2
- Spectral Graph Cut from a Filtering Point of View
- Accurate and Robust Fully-Automatic QCA: Method and Numerical Validation
- A General System for Supervised Biomedical Image Segmentation
- Surface–Region Context in Optimal Multi-Object Graph-based Segmentation: Robust Delineation of Pulmonary Tumors
- Enhancing the Potential of the Conventional Gaussian Mixture Model for Segmentation: from Images to Videos
- Automatic screening for tuberculosis in chest radiographs: a survey.
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- Understanding, Modeling and Detecting Brain Tumors : Graphical Models and Concurrent Segmentation/Registration methods
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