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- W2155336787 abstract "This paper presents an unsupervised category classification method for time-series images that combines incremental learning of Adaptive Resonance Theory-2 (ART-2) and self-mapping characteristic of Counter Propagation Networks (CPNs). Our method comprises the following procedures: 1) generating visual words using Self-Organizing Maps (SOM) from 128-dimensional descriptors in each feature point of a Scale-Invariant Feature Transform (SIFT), 2) forming labels using unsupervised learning of ART-2, and 3) creating and classifying categories on a category map of CPNs for visualizing spatial relations between categories. We use a vision system on a mobile robot for taking time-series images. Experimental results show that our method can classify objects into categories according to their change of appearance during the movement of a robot." @default.
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- W2155336787 date "2010-07-01" @default.
- W2155336787 modified "2023-09-25" @default.
- W2155336787 title "Unsupervised and adaptive category classification for a vision-based mobile robot" @default.
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- W2155336787 doi "https://doi.org/10.1109/ijcnn.2010.5596323" @default.
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