Image segmentation and uncertainty
Image segmentation and uncertainty
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
An approach to reducing the labeling cost of Markov random fields within an infinite label space
Signal Processing - Special section on digital signal processing for multimedia communications and services
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Class of Discrete Multiresolution Random Fields and Its Application to Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gauss-Markov random field models for texture segmentation
IEEE Transactions on Image Processing
A multiresolution image segmentation technique based on pyramidal segmentation and fuzzy clustering
IEEE Transactions on Image Processing
Unsupervised texture segmentation using multichannel decomposition and hidden Markov models
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A study of cloud classification with neural networks using spectral and textural features
IEEE Transactions on Neural Networks
Scalable multiresolution color image segmentation
Signal Processing
An affine symmetric image model and its applications
IEEE Transactions on Image Processing
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This work approaches the texture segmentation problem by incorporating Gibbs sampler (i.e., the combination of Markov random fields and simulated annealing) and a region-merging process within a multiresolution structure with "high class resolution and low boundary resolution" at high levels and "low class resolution and high boundary resolution" at lower ones. As the algorithm descends the multiresolution structure, the coarse segmentation results are propagated down to the lower levels so as to reduce the inherent class-boundary uncertainty and to improve the segmentation accuracy. The computational complexity and frequent occurrences of over-segmentation of Gibbs sampler are addressed and the computationally and functionally effective region-merging process is included to allow Gibbs sampler to start its annealing schedule at relatively low pseudo-temperature and to guide the search trajectory away from local minima associated with over-segmented configurations.