Diffusion-snakes:: Combining statistical shape knowledge and image information in a variational framework

被引：49

作者：

Cremers, D ^{[1
]}

Schnörr, C ^{[1
]}

Weickert, J ^{[1
]}

机构：

[1] Univ Mannheim, Dept Math & Comp Sci, Comp Vis Graph & Pattern Recognit Grp, D-68131 Mannheim, Germany

来源：

IEEE WORKSHOP ON VARIATIONAL AND LEVEL SET METHODS IN COMPUTER VISION, PROCEEDINGS | 2001年

关键词：

image segmentation; shape recognition; statistical learning; variational methods; diffusion; geodesic active contours; diffusion-snake;

D O I：

10.1109/VLSM.2001.938892

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a modification of the Mumford-Shah Junctional 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.

引用

页码：137 / 144

页数：8

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