Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
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
Unsupervised segmentation of noisy and textured images using Markov random fields
CVGIP: Graphical Models and Image Processing
Image segmentation based on oscillatory correlation
Neural Computation
Texture Classification Using Noncausal Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
Multiresolution Gauss-Markov random field models for texture segmentation
IEEE Transactions on Image Processing
Fast numerical integration of relaxation oscillator networks based on singular limit solutions
IEEE Transactions on Neural Networks
Texture segmentation using Gaussian-Markov random fields and neural oscillator networks
IEEE Transactions on Neural Networks
Locally excitatory globally inhibitory oscillator networks
IEEE Transactions on Neural Networks
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
Analysis of Microscopic Mast Cell Images Based on Network of Synchronised Oscillators
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
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Segmentation of textured images, a very important aspect of visual perception, remains still a challenging task for many image analysis problems. This paper presents a recently emerged segmentation method based on the temporary correlation theory. It proposes an explanation of visual scene analysis performed by human brain. Based on this theory, a network of locally connected synchronised oscillators is proposed for the image segmentation task. This oscillator network can be realised as a VLSI chip, providing very fast image segmentation. For texture description, the Gaussian Markov Random Field model widely used in many texture analysis tasks, is applied. The proposed method is applied to segment MRI images of human foot cross-section in order to detect bone structure. This analysis could be useful in osteoporosis diagnosis, allowing further evaluation of bone microarchitecture. The efficiency of the GMRF approach in bone texture modelling is demonstrated. The oscillator network method is compared with an ANN-based classifier. The segmentation results using both methods are presented and discussed.