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• W2156156737 abstract "In this paper a spatio-temporal analogic cellular neural network (CNN) algorithm is designed for front-end filtering, segmentation and object recognition. First, a generalized segmentation strategy is presented based on various diffusion models. Both PDE and non-PDE related schemes are discussed and their VLSI complexity is analyzed. In classification (object recognition) a CNN implementation of the autowave metric, a nonlinear variant of the Hausdorff metric, is used. This approach turned out to be superior compared to some other classification methods. A number of tests have been completed within the so-called bubble/debris segmentation experiments using original and artificial gray-scale images." @default.
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• W2156156737 date "2002-11-27" @default.
• W2156156737 modified "2023-09-25" @default.
• W2156156737 title "Spatio-temporal CNN algorithm for object segmentation and object recognition" @default.
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• W2156156737 doi "https://doi.org/10.1109/cnna.1998.685400" @default.
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