Texture segmentation using Gaussian Markov Random Fields and LEGION

An image segmentation method is proposed for texture analysis. The method is composed of two main parts. The first part determines a novel set of texture features based on Gaussian Markov Random Field (GMRF). Unlike other GMRF-based methods, our method is nor limited by a fixed set of texture types. The second part is LEGION (Locally Excitatory, Globally Inhibitory Oscillator Networks) which is a 2D array of neural oscillators. The coupling strengths between neighboring oscillators are calculated based on texture feature differences. When LEGION is simulated the oscillators corresponding to the same texture tend to oscillate in synchrony, whereas different texture regions tend to attain different phases. Results demonstrating the success of our method on real texture images are provided.