Work piece recognition based on the permutation neural classifier technique |
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Authors: | Gengis K Toledo Ernst Kussul Tatiana Baidyk |
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Affiliation: | (1) Center of Applied Research and Technological Development, Autonomous National University of Mexico (UNAM), Mexico, Mexico;(2) Dept. of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, USA |
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Abstract: | This article describes a permutation neural classifier technique for the object recognition problem. Our research is aimed
to help the automation of micromanufacturing and microassembly processes. In this article, we describe an object recognition
system based on permutation of codes and neural classifier technique. This approach is called permutation code neural classifier
(PCNC). In this work, we describe our experiments and results applying the PCNC in the recognition of micro work pieces. Two
databases with different images were used for the experiments. The authors have published these databases and encourage the
community to compare results. The best recognition rate obtained for the PCNC was of 97%. |
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