Texture classification using features whose effectiveness can be evaluated a priori
IEEE Transactions on Systems, Man and Cybernetics
Multispectral Random Field Models for Synthesis and Analysis of Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
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
Wavelet-based modeling of singular values for image texture classification
Machine Graphics & Vision International Journal
Image texture classification using wavelet packet transform and probabilistic neural network
Intelligent Data Analysis
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
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This work considers the problem of estimating parameters of multispectral random field (RF) image models using maximum likelihood (ML) methods. For images with an assumed Gaussian distribution, analytical results are developed for multispectral simultaneous autoregressive (MSAR) and Markov random field (MMRF) models which lead to practical procedures for calculating ML estimates. Although previous work has provided least squares methods for parameter estimation, the superiority of the ML method is evidenced by experimental results provided in this work. The effectiveness of multispectral RF models using ML estimates in modeling color texture images is also demonstrated.