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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Toledo, G.K., Kussul, E. & Baidyk, T. Work piece recognition based on the permutation neural classifier technique. Machine Vision and Applications 22, 495–504 (2011). https://doi.org/10.1007/s00138-010-0252-5
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DOI: https://doi.org/10.1007/s00138-010-0252-5