Bayesian Clustering for Unsupervised Estimation of Surface and Texture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation through Variable-Order Surface Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boundary Detection by Constrained Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Texture Segmentation Using Markov Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated Inspection of Textile Fabrics Using Textural Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generating Connected Textured Fractal Patterns Using Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Markov Random Field Model-Based Approach to Image Interpretation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Segmentation Methodology for Parametric Image Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parameter Estimation for Optimal Object Recognition: Theory andApplication
International Journal of Computer Vision
Bayesian Segmentation via Asymptotic Partition Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Color Image Segmentation for Multimedia Applications
Journal of Intelligent and Robotic Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximum entropy random fields for texture analysis
Pattern Recognition Letters
A Markov random field model for mode detection in cluster analysis
Pattern Recognition Letters
SAR image segmentation based on mixture context and wavelet hidden-class-label Markov random field
Computers & Mathematics with Applications
On texture and image interpolation using Markov models
Image Communication
Statistical Methods and Models for Video-Based Tracking, Modeling, and Recognition
Foundations and Trends in Signal Processing
Subsampling of Markov random fields
Journal of Visual Communication and Image Representation
Spatial color image segmentation based on finite non-Gaussian mixture models
Expert Systems with Applications: An International Journal
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The modeling and segmentation of images by MRF's (Markov random fields) is treated. These are two-dimensional noncausal Markovian stochastic processes. Two conceptually new algorithms are presented for segmenting textured images into regions in each of which the data are modeled as one of C MRF's. The algorithms are designed to operate in real time when implemented on new parallel computer architectures that can be built with present technology. A doubly stochastic representation is used in image modeling. Here, a Gaussian MRF is used to model textures in visible light and infrared images, and an autobinary (or autoternary, etc.) MRF to model a priori information about the local geometry of textured image regions. For image segmentation, the true texture class regions are treated either as a priori completely unknown or as a realization of a binary (or ternary, etc.) MRF. In the former case, image segmentation is realized as true maximum likelihood estimation. In the latter case, it is realized as true maximum a posteriori likelihood segmentation. In addition to providing a mathematically correct means for introducing geometric structure, the autobinary (or ternary, etc.) MRF can be used in a generative mode to generate image geometries and artificial images, and such simulations constitute a very powerful tool for studying the effects of these models and the appropriate choice of model parameters. The first segmentation algorithm is hierarchical and uses a pyramid-like structure in new ways that exploit the mutual dependencies among disjoint pieces of a textured region.