Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Level Set Model for Image Classification
International Journal of Computer Vision
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Unsupervised Non-parametric Region Segmentation Using Level Sets
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Influence of the Noise Model on Level Set Active Contour Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prior Knowledge, Level Set Representations & Visual Grouping
International Journal of Computer Vision
IEEE Transactions on Image Processing
Level Set Segmentation With Multiple Regions
IEEE Transactions on Image Processing
Unsupervised Variational Image Segmentation/Classification Using a Weibull Observation Model
IEEE Transactions on Image Processing
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In current active contour image segmentation methods, the number of regions is assumed to be known beforehand. It is related directly to a fixed number of active curves. How to allow it to vary is an important question which has been generally avoided. This study investigates a segmentation 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. The obtained evolution equations show that the proposed prior can cause some curves to disappear while other curves expand, thereby leading to a region merging by curve evolution, although not in the sense of the traditional one-step merging of two regions. We give a statistical interpretation to the coefficient of this prior to balance its effect systematically against the other functional terms. We show the validity and efficiency of the method by testing on real images of intensity. A comparison demonstrates the advantages of the proposed method over the region-competition algorithm in regard to the optimal number of regions and computational load.