A simplified approach to the HMM based texture analysis and its application to document segmentation
Pattern Recognition Letters
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
Support Vector Machines for Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Texture description using different wavelet transforms based on statistical parameters
WAV'08 Proceedings of the 2nd WSEAS International Conference on Wavelets Theory and Applications in Applied Mathematics, Signal Processing and Modern Science
Random Texture Defect Detection Using 1-D Hidden Markov Models Based on Local Binary Patterns
IEICE - Transactions on Information and Systems
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In this paper, we describe an automatic unsupervised texture segmentation scheme using hidden Markov models (HMMs). First, the feature map of the image is formed using Laws' micromasks and directional macromasks. Each pixel in the feature map is represented by a sequence of 4-D feature vectors. The feature sequences belonging to the same texture are modeled as an HMM. Thus, if there are M different textures present in an image, there are M distinct HMMs to be found and trained. Consequently, the unsupervised texture segmentation problem becomes an HMM-based problem, where the appropriate number of HMMs, the associated model parameters, and the discrimination among the HMMs become the foci of our scheme. A two-stage segmentation procedure is used. First, coarse segmentation is used to obtain the approximate number of HMMs and their associated model parameters. Then, fine segmentation is used to accurately estimate the number of HMMs and the model parameters. In these two stages, the critical task of merging the similar HMMs is accomplished by comparing the discrimination information (DI) between the two HMMs against a threshold computed from the distribution of all DI's. A postprocessing stage of multiscale majority filtering is used to further enhance the segmented result. The proposed scheme is highly suitable for pipeline/parallel implementation. Detailed experimental results are reported. These results indicate that the present scheme compares favorably with respect to other successful schemes reported in the literature