Layered 4D Representation and Voting for Grouping from Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Voting-Based Computational Framework for Visual Motion Analysis and Interpretation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Globally adaptive region information for automatic color-texture image segmentation
Pattern Recognition Letters
Environmental Modelling & Software
Local Adaptivity to Variable Smoothness for Exemplar-Based Image Regularization and Representation
International Journal of Computer Vision
Viewpoint Invariant Texture Description Using Fractal Analysis
International Journal of Computer Vision
Computers & Mathematics with Applications
Bayesian non-local means filter, image redundancy and adaptive dictionaries for noise removal
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Scale-space texture description on SIFT-like textons
Computer Vision and Image Understanding
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Many studies have proven that statistical model-based texture segmentation algorithms yield good results provided that the model parameters and the number of regions be known a priori. In this correspondence, we present an unsupervised texture segmentation method that does not require knowledge about the different texture regions, their parameters, or the number of available texture classes. The proposed algorithm relies on the analysis of local and global second and higher order spatial statistics of the original images. The segmentation map is modeled using an augmented-state Markov random field, including an outlier class that enables dynamic creation of new regions during the optimization process. A Bayesian estimate of this map is computed using a deterministic relaxation algorithm. Results on real-world textured images are presented