Multiple Resolution Segmentation of Textured Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wavelets and subband coding
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
Embedded image coding using zerotrees of wavelet coefficients
IEEE Transactions on Signal Processing
Singularity detection and processing with wavelets
IEEE Transactions on Information Theory - Part 2
Multiscale Bayesian segmentation using a trainable context model
IEEE Transactions on Image Processing
Multiscale image segmentation using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
A multiscale random field model for Bayesian image segmentation
IEEE Transactions on Image Processing
A new strategy for urinary sediment segmentation based on wavelet, morphology and combination method
Computer Methods and Programs in Biomedicine
Multiscale fusion of wavelet-domain hidden Markov tree through graph cut
Image and Vision Computing
Minimum classification error learning for sequential data in the wavelet domain
Pattern Recognition
Multiscale information fusion by graph cut through convex optimization
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Global optimization of wavelet-domain hidden Markov tree for image segmentation
Pattern Recognition
Wavelet energy signature: comparison and analysis
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
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We propose a method based on the Hidden Markov Tree (HMT) model for multiscale image segmentation in the wavelet domain. We use the inherent tree structure of the model to segment the image at a range of different scales. We then merge these different scale segmented images using boundary refinement conditions. The final segmented image utilizes the reliability of coarse scale segmented images and the fineness of finer scales segmented images. We demonstrate the performance of the algorithm on synthetic data and aerial photos.