This paper presents GeoS, a new algorithm for the e\ufb03cient segmentation of n-dimensional image and video data. The segmentation problem is cast as approximate energy minimization in a conditional random \ufb01eld. A new, parallel \ufb01ltering operator built upon e\ufb03cient geodesic distance computation is used to propose a set of spatially smooth, contrast-sensitive segmentation hypotheses. An economical search algorithm \ufb01nds the solution with minimum energy within a sensible and highly restricted subset of all possible labellings. Advantages include: i) computational e\ufb03ciency with high segmentation accuracy; ii) the ability to estimate an approximation to the posterior over segmentations; iii) the ability to handle generally complex energy models. Comparison with max-\ufb02owindicates upto60 times greater computational e\ufb03ciency as well as greater memory e\ufb03ciency. GeoS is validated quantitatively and qualitatively by thorough comparative experiments on existing and novel ground-truth data. Numerous results on interactive and automatic segmentation of photographs, video and volumetric medical image data are presented.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper presents GeoS, a new algorithm for the e\ufb03cient segmentation of n-dimensional image and video data. The segmentation problem is cast as approximate energy minimization in a conditional random \ufb01eld. A new, parallel \ufb01ltering operator built upon e\ufb03cient geodesic distance computation is used to propose a set of spatially smooth, contrast-sensitive segmentation hypotheses. 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