Texture segmentation with a neural network

A neural method for gray value segmentation now is applied to texture segmentation. The parallel-sequential algorithm is based on recursive nonlinear feature smoothing in a 4-neighborhood. The smoothed feature values then can be segmented using an adaptive adjacency criterion which defines a special graph structure, called the Feature Similarity Graph. The segments are the connected components of that graph. The combination of results from the different image features is done in a hierarchical process starting, like in the human visual system, with gray value segmentation. Besides segments with homogeneous gray value this process also provides texture elements which are the basis for the calculation of new image features. Then, first, the modulus of the gray value gradient is used as a new feature of the original image. The following segmentation basing on that feature provides regions which are homogeneous with respect to the mean gray value gradient. Furthermore, texel directions are calculated. That feature contains information on texture orientation of textured image regions. With these features the same neural segmentation method is able to separate not only regions with different mean gray values but also those with different textures.