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Fuzzy pairwise Markov chain to segment correlated noisy data
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SAR image segmentation based on mixture context and wavelet hidden-class-label Markov random field
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Multiscale fusion of wavelet-domain hidden Markov tree through graph cut
Image and Vision Computing
Texture map: an effective representation for image segmentation
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Modelling Stem Cells Lineages with Markov Trees
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
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EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
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Image and Vision Computing
IEEE Transactions on Image Processing
Stochastic texture analysis for monitoring stochastic processes in industry
Pattern Recognition Letters
Color texture analysis using the wavelet-based hidden Markov model
Pattern Recognition Letters
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ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
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BEBI'09 Proceedings of the 2nd WSEAS international conference on Biomedical electronics and biomedical informatics
Artificial immune multi-objective SAR image segmentation with fused complementary features
Information Sciences: an International Journal
Global optimization of wavelet-domain hidden Markov tree for image segmentation
Pattern Recognition
Texture segmentation using neural networks and multi-scale wavelet features
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
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ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Automatic texture segmentation based on wavelet-domain hidden markov tree
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
A fully unsupervised image segmentation algorithm based on wavelet-domain hidden markov tree models
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Neural network based texture segmentation using a markov random field model
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Texture segmentation using SOM and multi-scale bayesian estimation
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
MR image segmentation using phase information and a novel multiscale scheme
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Contourlet based multiresolution texture segmentation using contextual hidden markov models
CIT'04 Proceedings of the 7th international conference on Intelligent Information Technology
A new wavelet-domain HMTseg algorithm for remotely sensed image segmentation
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Unsupervised image segmentation using contourlet domain hidden markov trees model
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
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Multiscale texture segmentation via a contourlet contextual hidden Markov model
Digital Signal Processing
Multiscale discriminant saliency for visual attention
ICCSA'13 Proceedings of the 13th international conference on Computational Science and Its Applications - Volume 1
Edge multi-scale markov random field model based medical image segmentation in wavelet domain
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
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We introduce a new image texture segmentation algorithm, HMTseg, based on wavelets and the hidden Markov tree (HMT) model. The HMT is a tree-structured probabilistic graph that captures the statistical properties of the coefficients of the wavelet transform. Since the HMT is particularly well suited to images containing singularities (edges and ridges), it provides a good classifier for distinguishing between textures. Utilizing the inherent tree structure of the wavelet HMT and its fast training and likelihood computation algorithms, we perform texture classification at a range of different scales. We then fuse these multiscale classifications using a Bayesian probabilistic graph to obtain reliable final segmentations. Since HMTseg works on the wavelet transform of the image, it can directly segment wavelet-compressed images without the need for decompression into the space domain. We demonstrate the performance of HMTseg with synthetic, aerial photo, and document image segmentations