Segmentation of MRI trabecular-bone images using network of synchronised oscillators
Machine Graphics & Vision International Journal
Recurrent network with large representational capacity
Neural Computation
Image Segmentation by Networks of Spiking Neurons
Neural Computation
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
Image segmentation using local spectral histograms and linear regression
Pattern Recognition Letters
A robust region-adaptive dual image watermarking technique
Journal of Visual Communication and Image Representation
Multiscale Texture Extraction with Hierarchical (BV,Gp,L2) Decomposition
Journal of Mathematical Imaging and Vision
Automatic classification of the interferential tear film lipid layer using colour texture analysis
Computer Methods and Programs in Biomedicine
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We propose an image segmentation method based on texture analysis. Our method is composed of two parts. The first part determines a novel set of texture features derived from a Gaussian-Markov random fields (GMRF) model. Unlike a GMRF-based approach, our method does not employ model parameters as features or require the extraction of features for a fixed set of texture types a priori. The second part is a 2D array of locally excitatory globally inhibitory oscillator networks (LEGION). After being filtered for noise suppression, features are used to determine the local couplings in the network. When LEGION runs, the oscillators corresponding to the same texture tend to synchronize, whereas different texture regions tend to correspond to distinct phases. In simulations, a large system of differential equations is solved for the first time using a recently proposed method for integrating relaxation oscillator networks. We provide results on real texture images to demonstrate the performance of our method