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Finding Dots: Segmentation as Popping out Regions from Boundaries
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Elena Bernardis and Stella X. Yu
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IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, California, 13-18 June 2010
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Paper
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Poster
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Abstract
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Many applications need to segment out all small round regions in an image. This task of finding dots can be viewed as a region segmentation problem where the dots form one region and the areas between dots form the other. We formulate it as a graph cuts problem with two types of grouping cues: short-range attraction based on feature similarity and long-range repulsion based on feature dissimilarity. The feature we use is a pixel-centric relational representation that encodes local convexity: Pixels inside the dots and outside the dots become sinks and sources of the feature vector. Normalized cuts on both attraction and repulsion pop out all the dots in a single binary segmentation. Our experiments show that our method is more accurate and robust than state-of-art segmentation algorithms on four categories of microscopic images. It can also detect textons in natural scene images with the same set of parameters.
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Keywords
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image segmentation, spectral graph partitioning, watershed, attraction and repulsion
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