Dynamic Trees for Unsupervised Segmentation and Matching of Image Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image compression based on a family of stochastic models
Signal Processing
A statistical framework based on a family of full range autoregressive models for edge extraction
Pattern Recognition Letters
Multispectral image segmentation by a multichannel watershed-based approach
International Journal of Remote Sensing
A robust hidden Markov Gauss mixture vector quantizer for a noisy source
IEEE Transactions on Image Processing
Hierarchical multiple Markov chain model for unsupervised texture segmentation
IEEE Transactions on Image Processing
Automatic image segmentation by dynamic region growth and multiresolution merging
IEEE Transactions on Image Processing
A hierarchical texture model for unsupervised segmentation of remotely sensed images
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
A computer-aided detection system for clustered microcalcifications
Artificial Intelligence in Medicine
Learning conditional random fields for classification of hyperspectral images
IEEE Transactions on Image Processing
Evolutionary generation of prototypes for a learning vector quantization classifier
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Interactive shadow removal from a single image using hierarchical graph cut
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
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We present a new image segmentation algorithm based on a tree-structured binary MRF model. The image is recursively segmented in smaller and smaller regions until a stopping condition, local to each region, is met. Each elementary binary segmentation is obtained as the solution of a MAP estimation problem, with the region prior modeled as an MRF. Since only binary fields are used, and thanks to the tree structure, the algorithm is quite fast, and allows one to address the cluster validation problem in a seamless way. In addition, all field parameters are estimated locally, allowing for some spatial adaptivity. To improve segmentation accuracy, a split-and-merge procedure is also developed and a spatially adaptive MRF model is used. Numerical experiments on multispectral images show that the proposed algorithm is much faster than a similar reference algorithm based on "flat" MRF models, and its performance, in terms of segmentation accuracy and map smoothness, is comparable or even superior.