Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Active vision
A variational level set approach to multiphase motion
Journal of Computational Physics
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Coupled Geodesic Active Regions for Image Segmentation: A Level Set Approach
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Geodesic active regions and level set methods for motion estimation and tracking
Computer Vision and Image Understanding
Towards recognition-based variational segmentation using shape priors and dynamic labeling
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Exploit camera metadata for enhancing interesting region detection and photo retrieval
Multimedia Tools and Applications
On-Line, incremental learning of a robust active shape model
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
A multiple object geometric deformable model for image segmentation
Computer Vision and Image Understanding
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Level set based segmentation has been used with and without shape priors, to approach difficult segmentation problems in several application areas. This paper addresses two limitations of the classical level set based segmentation approaches: They usually deliver just two regions – one foreground and one background region, and if some prior information is available, they are able to take into account just one prior but not more. In these cases, one object of interest is reconstructed but other possible objects of interest and unfamiliar image structures are suppressed. The approach we propose in this paper can simultaneously handle an arbitrary number of regions and competing shape priors. Adding to that, it allows the integration of numerous pose invariant shape priors, while segmenting both known and unknown objects. Unfamiliar image structures are considered as separate regions. We use a region splitting to obtain the number of regions and the initialization of the required level set functions. In a second step, the energy of these level set functions is robustly minimized and similar regions are merged in a last step. All these steps are considering given shape priors. Experimental results demonstrate the method for arbitrary numbers of regions and competing shape priors.