We present a novel approach to image segmentation using iterated Graph Cuts based on multi-scale smoothing. We compute the prior probability obtained by the likelihood from a color histogram and a distance transform using the segmentation results from graph cuts in the previous process, and set the probability as the t-link of the graph for the next process. The proposed method can segment the regions of an object with a stepwise process from global to local segmentation by iterating the graph-cuts process with Gaussian smoothing using different values for the standard deviation. We demonstrate that we can obtain 4.7% better segmentation than that with the conventional approach.
Image Segmentation Using Iterated Graph Cuts Based on Multi-scale Smoothing
Asian Conference on Computer Vision ; 2007 ; Tokyo, Japan November 18, 2007 - November 22, 2007
01.01.2007
11 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Image Segmentation , Prior Probability , Gaussian Mixture Model , Segmentation Result , Color Histogram Computer Science , Image Processing and Computer Vision , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Artificial Intelligence , Biometrics , Algorithm Analysis and Problem Complexity
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