Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
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
International Journal of Computer Vision
Normalized Cuts and 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
VLSM '01 Proceedings of the IEEE Workshop on Variational and Level Set Methods (VLSM'01)
Geometric Level Set Methods in Imaging,Vision,and Graphics
Geometric Level Set Methods in Imaging,Vision,and Graphics
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
International Journal of Computer Vision
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
IEEE Transactions on Image Processing
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Variational frameworks based on level set methods are popular for the general problem of image segmentation. They combine different feature channels in an energy minimization approach. In contrast to other popular segmentation frameworks, e.g. the graph cut framework, current level set formulations do not allow much user interaction. Except for selecting the initial boundary, the user is barely able to guide or correct the boundary propagation. Based on Dempster-Shafer theory of evidence we propose a segmentation framework which integrates user interaction in a novel way. Given the input image, the proposed algorithm determines the best segmentation allowing the user to take global influence on the boundary propagation.