A Statistical Overlap Prior for Variational Image Segmentation
International Journal of Computer Vision
Effective level set image segmentation with a kernel induced data term
IEEE Transactions on Image Processing
A level set method based on the Bayesian risk for medical image segmentation
Pattern Recognition
Fast Approximate Energy Minimization with Label Costs
International Journal of Computer Vision
A continuous max-flow approach to minimal partitions with label cost prior
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
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In current level set image segmentation methods, the number of regions is assumed to known beforehand. As a result, it remains constant during the optimization of the objective functional. How to allow it to vary is an important question which has been generally avoided. This study investigates a region merging prior related to regions area to allow the number of regions to vary automatically during curve evolution, thereby optimizing the objective functional implicitly with respect to the number of regions. We give a statistical interpretation to the coefficient of this prior to balance its effect systematically against the other functional terms. We demonstrate the validity and efficiency of the method by testing on real images of intensity, color, and motion.