Human silhouette extraction method using region based background subtraction
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
Segmentation of sub-cortical structures by the graph-shifts algorithm
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Image segmentation by MAP-ML estimations
IEEE Transactions on Image Processing
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ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Accurate foreground extraction using graph cut with trimap estimation
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
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IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
SOM and fuzzy based color image segmentation
Multimedia Tools and Applications
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This paper presents an efficient algorithm for image segmentation and a framework for perceptual grouping. It makes an attempt to provide one way of combining bottom-up and top-down approaches. In image segmentation, it generalizes the Swendsen-Wang cut algorithm [1] (SWC) to make both 2-way and m-way cuts, and includes topology change processes (graph repartitioning and boundary diffusion). The method directly works at a low temperature without using annealing. We show that it is much faster than the DDMCMC approach [12] and more robust than the SWC method. The results are demonstrated on the Berkeley data set [7]. In perceptual grouping, it integrates discriminative model learning/computing, a belief propagation algorithm (BP) [15] , and SWC into a three-layer computing framework. These methods are realized as different levels of approximation to an "ideal" generative model. We demonstrate the algorithm on the problem of human body configuration.