Title :
Diffusion-snakes: combining statistical shape knowledge and image information in a variational framework
Author :
Cremers, Daniel ; Schnörr, Christoph ; Weickert, Joachim
Author_Institution :
Dept. of Math. & Comput. Sci., Mannheim Univ., Germany
Abstract :
We present a modification of the Mumford-Shah functional and its cartoon limit which allows the incorporation of statistical shape knowledge in a single energy functional. We show segmentation results on artificial and real-world images with and without prior shape information. In the case of occlusion and strongly cluttered background the shape prior significantly improves segmentation. Finally we compare our results to those obtained by a level-set implementation of geodesic active contours
Keywords :
computational geometry; functional equations; image segmentation; statistical analysis; variational techniques; Mumford-Shah functional; cartoon limit; cluttered background; diffusion snakes; energy functional; image information; image segmentation; occlusion; statistical shape knowledge; variational framework; Active contours; Active shape model; Computer graphics; Computer vision; Equations; Image segmentation; Level set; Shape control; Spline; Statistical learning;
Conference_Titel :
Variational and Level Set Methods in Computer Vision, 2001. Proceedings. IEEE Workshop on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7695-1278-X
DOI :
10.1109/VLSM.2001.938892
