Multiple channel neural network model for texture classification and segmentation

A computational image analysis model that resembles the functioning of the brain is introduced. The multiple-channel neural network model consists of three stages: multiple-channel representation, neural network classification and spatial context correction. The model is implemented and applied to the problem of texture analysis. Gabor filters are involved to represent the textural patterns. Low misclassification rates are obtained. Composite textural images are also applied to the system and accurately segmented images are obtained. The usefulness of the model is demonstrated