Graph Cuts and Efficient N-D Image Segmentation

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Graph Cuts and Efficient N-D Image Segmentation
Combinatorial graph cut algorithms have been successfully applied to a wide range of problems in vision and graphics. This paper focusses on possibly the simplest application of graph-cuts: segmentation of objects in image data. Despite its simplicity, 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. Graph cuts based approaches to object extraction have also been shown to have interesting connections with earlier segmentation methods such as snakes, geodesic active contours, and level-sets. The segmentation energies optimized by graph cuts combine boundary regularization with region-based properties in the same fashion as Mumford-Shah style functionals. We present motivation and detailed technical description of the basic combinatorial optimi...
Yuri Boykov, Gareth Funka-Lea
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2006
Where IJCV
Authors Yuri Boykov, Gareth Funka-Lea
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