Boundary Detection by Constrained Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multichannel Texture Analysis Using Localized Spatial Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Resolution Segmentation of Textured Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification of Rotated and Scaled Textured Images Using Gaussian Markov Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image analysis and computer vision: 1992
CVGIP: Image Understanding
A review of recent texture segmentation and feature extraction techniques
CVGIP: Image Understanding
Digital Image Processing
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We present a Markov random field (MRF) model for digital imagescapable of representing anisotropic textures with arbitraryorientations. The discrete Hamiltonian is obtained through finitedifference discretization of a continuous elliptic operator on R², together with a polynomial perturbation.We present the solution of a non-linear system of algebraicequations that estimates the orientation angle and theelliptic operator parameters in terms of the estimated discreteHamiltonian parameters.We perform experiments of simulation and retrieval of parametersusing, respectively, the Gibbssampler algorithm and the variational estimators for MRF. Weuse also the estimation algorithm to identify relative rotationof digital images of the same realisticpicture scanned at various different orientations.