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21 September 2001 New second-order difference algorithm for image segmentation based on cellular neural networks (CNNs)
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Proceedings Volume 4550, Image Extraction, Segmentation, and Recognition; (2001) https://doi.org/10.1117/12.441485
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China
Abstract
Image segmentation is one of the most important operations in many image analysis problems, which is the process that subdivides an image into its constituents and extracts those parts of interest. In this paper, we present a new second order difference gray-scale image segmentation algorithm based on cellular neural networks. A 3x3 CNN cloning template is applied, which can make smooth processing and has a good ability to deal with the conflict between the capability of noise resistance and the edge detection of complex shapes. We use second order difference operator to calculate the coefficients of the control template, which are not constant but rather depend on the input gray-scale values. It is similar to Contour Extraction CNN in construction, but there are some different in algorithm. The result of experiment shows that the second order difference CNN has a good capability in edge detection. It is better than Contour Extraction CNN in detail detection and more effective than the Laplacian of Gauss (LOG) algorithm.
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Shukai Meng and Yu Long Mo "New second-order difference algorithm for image segmentation based on cellular neural networks (CNNs)", Proc. SPIE 4550, Image Extraction, Segmentation, and Recognition, (21 September 2001); https://doi.org/10.1117/12.441485
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