Normalized Cuts and Image Segmentation
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Geometric Level Set Methods in Imaging,Vision,and Graphics
Geometric Level Set Methods in Imaging,Vision,and Graphics
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Spatially coherent clustering using graph cuts
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Supervised texture detection in images
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
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Knowledge from markers in watershed segmentation
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Universal seed skin segmentation
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Systematic skin segmentation: merging spatial and non-spatial data
Multimedia Tools and Applications
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We present a method that automatically partitions a single image into non-overlapping regions coherent in texture and colour. An assumption that each textured or coloured region can be represented by a small template, called the seed, is used. Positioning of the seed across the input image gives many possible sub-segmentations of the image having same texture and colour property as the pixels behind the seed. A probability map constructed during the sub-segmentations helps to assign each pixel to just one most probable region and produce the final pyramid representing various detailed segmentations at each level. Each sub-segmentation is obtained as the min-cut/max-flow in the graph built from the image and the seed. One segment may consist of several isolated parts. Compared to other methods our approach does not need a learning process or a priori information about the textures in the image. Performance of the method is evaluated on images from the Berkeley database.