1 February 1998 Dot pattern clustering using a cellular neural network
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A recurrent cellular neural network (CNN) for dot pattern clustering is presented. The CNN is based on dot pattern smoothing with a recursive algorithm which updates the dot positions by summing up nonlinear functions of position differences of Voronoi neighbors in each step. A perception criterion which assesses the proximity of dots by comparing their distance with an adaptive threshold is used for weighting the position difference sand for the generation of a special graph, the Dot Proximity Graph (DPG). The clusters are the connected components of the DPG. The method explains the Gestalt phenomenon and often corresponds with human perception which is demonstrated by representative examples. A possibility to deal with cluster hierarchies and dot textures is opened. Adequate cluster descriptions, especially a simple but powerful shape description, and a sequence of CNN's are needed for that purpose.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Herbert Jahn, "Dot pattern clustering using a cellular neural network", Proc. SPIE 3346, Sixth International Workshop on Digital Image Processing and Computer Graphics: Applications in Humanities and Natural Sciences, (1 February 1998); doi: 10.1117/12.301379; https://doi.org/10.1117/12.301379

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