Mumford-Shah regularizer with contextual feedback
Journal of Mathematical Imaging and Vision
Hierarchical multiple Markov chain model for unsupervised texture segmentation
IEEE Transactions on Image Processing
Segmentation subject to stitching constraints: finding many small structures in a large image
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Evaluation method for MRI brain tissue abnormalities segmentation study
Proceedings of the 15th WSEAS international conference on Computers
Patch-Based texture edges and segmentation
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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In this paper we introduce a novel optimization framework for hierarchical data clustering and apply it to the problem of unsupervised texture segmentation. The proposed objective function assesses the quality of an image partitioning simultaneously at different resolution levels and yields a sequence of consistently nested image segmentations. A novel model selection criterion to select significant image structures from various scales is proposed. As an efficient deterministic optimization heuristic a mean-field annealing algorithm is derived.