A practical approach to continuous-tone color image segmentation is proposed. Unlike traditional algorithms of image segmentation which tend to use threshold methods we intend to show how neural network technique can be successfully applied to this problem. We used a back- propagation network architecture in this work. It was assumed that each image pixel has its own color, which is somehow correlated with those of the nearest neighborhood. To describe the color properties of a certain neighborhood we suggested nine component feature vectors for every image pixel. This set of feature components is applied to the network input neurons. By this means, every image pixel is described by the following values R, G and B (color intensities), Mr, Mg and Mb (averages of intensities of the nearest neighborhood), (sigma) r, (sigma) gland (sigma) b (r.m.s. deviations of color intensities). To estimate the algorithm efficiency the scalar criterion was proposed. It was shown by the results of comparative experiment that neural segmentation provides more efficiency than that of traditional, using threshold methods.
A practical approach to continuous-tone color image transformation into the image with a finite number of colors is proposed. To achieve this the self organized network was explored. The basic feature of this network provides its self-learning by the competition between several hypotheses (colors) about the analyzed image and the most authentic hypothesis overcomes. Unlike traditional algorithms of color separation which tend to analyze the quantitative contribution of different color components (red, green, blue) to the initial image, the proposed network moreover takes into consideration the qualitative, statistical character of their distribution. To make the operation of self training in learning mode for this network more accurate the output neurons are connected additionally by lateral excitement connections. We proposed the estimate to measure the algorithm efficiency and similarity between input and output images, which gives the possibility to compare our method with other well-known methods. Our method is useful in the fields of precision color image analysis and understanding.
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