Split Bregman Method for Minimization of Region-Scalable Fitting Energy for Image Segmentation

Thursday, March 10, 2011 - 11:30am - 12:30pm
Keller 3-180
Chiu-Yen Kao (The Ohio State University)
In this talk, we introduce the segmentation method which incorporates the global convex segmentation method and the split Bregman technique into the region-scalable fitting energy model. The new proposed method based on the region-scalable model can draw upon intensity information in local regions at a controllable scale, so that it can segment images with intensity inhomogeneity. Furthermore, with the application of the global convex segmentation method and the split Bregman technique, the method is very robust and efficient. By using a non-negative edge detector function to the proposed method, the algorithm can detect the boundaries more easily and achieve results that are very similar to those obtained through the classical geodesic active contour model. Experimental results for synthetic and real images have shown the robustness and efficiency of our method and also demonstrated the desirable advantages of the proposed method.
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