Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximum Likelihood Estimation Methods for Multispectral Random Field Image Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture modelling by discrete distribution mixtures
Computational Statistics & Data Analysis
Model-Based Fatique Fractographs Texture Analysis
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
International Journal of Computer Vision
A high-performance VLSI architecture for the histogram peak-climbing data clustering algorithm
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Image compression based on a family of stochastic models
Signal Processing
Color image coding using regional correlation of primary colors
Image and Vision Computing
Extreme Compression and Modeling of Bidirectional Texture Function
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Discrete Mixtures Colour Texture Models
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Advanced textural representation of materials appearance
SIGGRAPH Asia 2011 Courses
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Bidirectional texture function simultaneous autoregressive model
MUSCLE'11 Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding
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In this paper, multispectral extensions to the traditional gray level simultaneous autoregressive (SAR) and Markov random field (MRF) models are considered. Furthermore, a new image model is proposed, the pseudo-Markov model, which retains the characteristics of the multispectral Markov model, yet admits to a simplified parameter estimation method. These models are well-suited to analysis and modeling of color images. For each model considered, procedures are developed for parameter estimation and image synthesis. Experimental results, based on known image models and natural texture samples, substantiate the validity of these results.