Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Priors for Level Set Representations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Matching Distance Functions: A Shape-to-Area Variational Approach for Global-to-Local Registration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Geometric Level Set Methods in Imaging,Vision,and Graphics
Geometric Level Set Methods in Imaging,Vision,and Graphics
Natural Image Statistics for Natural Image Segmentation
International Journal of Computer Vision
Motion Competition: A Variational Approach to Piecewise Parametric Motion Segmentation
International Journal of Computer Vision
International Journal of Computer Vision
A comparative evaluation of interactive segmentation algorithms
Pattern Recognition
IEEE Transactions on Image Processing
Interactive image segmentation using level sets and Dempster-Shafer theory of evidence
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
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Image segmentation is the process of partitioning an image into at least two regions. Usually, active contours or level set based image segmentation methods combine different feature channels, arising from the color distribution, texture or scale information, in an energy minimization approach. In this paper, we integrate the Dempster-Shafer evidence theory in level set based image segmentation to fuse the information (and resolve conflicts) arising from different feature channels. They are further combined with a smoothing term and applied to the signed distance function of an evolving contour. In several experiments we demonstrate the properties and advantages of using the Dempster-Shafer evidence theory in level set based image segmentation.