Unsupervised texture image segmentation by improved neural network ART2
Wang, Zhiling ; Labini, G. Sylos ; Mugnuolo, R. ; et al.
Mar - 1994

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type: Conference Proceedings

Abstract
We here propose a segmentation algorithm of texture image for a computer vision system on a space robot. An improved adaptive resonance theory (ART2) for analog input patterns is adapted to classify the image based on a set of texture image features extracted by a fast spatial gray level dependence method (SGLDM). The nonlinear thresholding functions in input layer of the neural network have been constructed by two parts: firstly, to reduce the effects of image noises on the features, a set of sigmoid functions is chosen depending on the types of the feature; secondly, to enhance the contrast of the features, we adopt fuzzy mapping functions. The cluster number in output layer can be increased by an autogrowing mechanism constantly when a new pattern happens. Experimental results and original or segmented pictures are shown, including the comparison between this approach and K-means algorithm. The system written in C language is performed on a SUN-4/330 sparc-station with an image board IT-150 and a CCD camera.

keywords : ALGORITHMS,COMPUTER VISION,FUZZY SYSTEMS,IMAGE CLASSIFICATION,IMAGE ENHANCEMENT,NEURAL NETS,PATTERN RECOGNITION,ROBOT SENSORS,TEXTURES