Geodesic Active Regions for Supervised Texture Segmentation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Lazy texture selection based on active learning
The Visual Computer: International Journal of Computer Graphics
Interactive Image Segmentation Based on Level Sets of Probabilities
IEEE Transactions on Visualization and Computer Graphics
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In this technical sketch, we adopt the level set method for image segmentation that integrates region statistics and edge responses. It is well-known that a serious limitation of existing level set algorithms for image segmentation is that the final result is sensitive to the location of the initialization. This is because level set evolution is typically driven by forces computed from local image data. We overcome this problem by adopting a novel level set function based on foreground probabilities, and further integrating the level set method with a probabilistic pixel classifier [Liu and Yu 2012]. Since an accurate classifier does not exist at the beginning, the segmentation framework is based on the expectation-maximization (EM) algorithm. In summary, the motivations for our method based on level sets of probabilities are manifold.