The application of Markov random field models to wavelet-based image denoising
Imaging and vision systems
Double random field models for remote sensing image segmentation
Pattern Recognition Letters
Scalable multiresolution color image segmentation
Signal Processing
Image compression based on a family of stochastic models
Signal Processing
A clustering method based on multidimensional texture analysis
Pattern Recognition
A statistical framework based on a family of full range autoregressive models for edge extraction
Pattern Recognition Letters
Object-based and semantic image segmentation using MRF
EURASIP Journal on Applied Signal Processing
Local relational string and mutual matching for image retrieval
Information Processing and Management: an International Journal
Multiresolution image parametrization for improving texture classification
EURASIP Journal on Advances in Signal Processing
Improved bi-dimensional EMD and Hilbert spectrum for the analysis of textures
Pattern Recognition
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
IEEE Transactions on Image Processing
Gaussian multiresolution models: exploiting sparse Markov and covariance structure
IEEE Transactions on Signal Processing
MRF-MBNN: a novel neural network architecture for image processing
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Unsupervised multiresolution segmentation of SAR imagery based on region-based hierarchical model
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Multi-scale stochastic simulation with a wavelet-based approach
Computers & Geosciences
Median binary pattern for textures classification
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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We present a new algorithm for segmentation of textured images using a multiresolution Bayesian approach. The new algorithm uses a multiresolution Gaussian autoregressive (MGAR) model for the pyramid representation of the observed image, and assumes a multiscale Markov random field model for the class label pyramid. The models used in this paper incorporate correlations between different levels of both the observed image pyramid and the class label pyramid. The criterion used for segmentation is the minimization of the expected value of the number of misclassified nodes in the multiresolution lattice. The estimate which satisfies this criterion is referred to as the “multiresolution maximization of the posterior marginals” (MMPM) estimate, and is a natural extension of the single-resolution “maximization of the posterior marginals” (MPM) estimate. Previous multiresolution segmentation techniques have been based on the maximum a posterior (MAP) estimation criterion, which has been shown to be less appropriate for segmentation than the MPM criterion. It is assumed that the number of distinct textures in the observed image is known. The parameters of the MGAR model-the means, prediction coefficients, and prediction error variances of the different textures-are unknown. A modified version of the expectation-maximization (EM) algorithm is used to estimate these parameters. The parameters of the Gibbs distribution for the label pyramid are assumed to be known. Experimental results demonstrating the performance of the algorithm are presented