Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional
International Journal of Computer Vision
Using Prior Shapes in Geometric Active Contours in a Variational Framework
International Journal of Computer Vision
Shape Priors for Level Set Representations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
A Bayesian Estimation of Building Shape Using MCMC
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Nonlinear Shape Statistics in Mumford-Shah Based Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Nonlinear Shape Statistics via Kernel Spaces
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
International Journal of Computer Vision
Three-Dimensional Shape Knowledge for Joint Image Segmentation and Pose Tracking
International Journal of Computer Vision
Prior Knowledge, Level Set Representations & Visual Grouping
International Journal of Computer Vision
Object detection by global contour shape
Pattern Recognition
A Perturbation Suppressing Segmentation Technique Based on Adaptive Diffusion
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
New Possibilities with Sobolev Active Contours
International Journal of Computer Vision
Stabilization of parametric active contours using a tangential redistribution term
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
Object detection by contour segment networks
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
From inpainting to active contours
VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
Ultrasound kidney segmentation with a global prior shape
Journal of Visual Communication and Image Representation
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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.