Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
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
A Variational Framework for Active and Adaptative Segmentation of Vector Valued Images
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
IEEE Transactions on Image Processing
Robust Tracking by Means of Template Adaptation with Drift Correction
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
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In this paper we present an approach for multi-dimensional histogram-based image segmentation. We combine level-set methods for image segmentation with probabilistic region descriptors based on multi-dimensional histograms. Unlike stated by other authors we show that colour space histograms provide a reasonable and efficient description of image regions. In contrast to Gaussian Mixture Model based algorithms no parameter learning and estimation of the number of mixture components is required. Compared to recent level-set based segmentation methods satisfying segmentation results are achieved without specific features (e.g. texture). In a comparison with state-of-the-art image segmentation methods it is shown that the proposed approach yields competitive results.