Real-life application case studies using CMOS 0.8μm CNN universal chip: Analogic algorithm for motion detection and texture segmentation

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Abstract

Using a 20*22 CNN Universal Machine chip two application case studies will be presented. A new analogic CNN algorithm will be shown to detect objects having larger size than a given value on black-and-white image sequences moving in a given range of direction and speed (17 μs processing speed could be achieved). An extremely fast texture classification analogic algorithm will be given next with approximately 2 μs processing speed and with less than 5% misclassification error rate for 4 natural textures in real-life testing environment.

Original languageEnglish
Pages363-368
Number of pages6
Publication statusPublished - Dec 1 1996
EventProceedings of the 1996 4th IEEE International Workshop on Cellular Neural Networks, and Their Applications, CNNA-96 - Seville, Spain
Duration: Jun 24 1996Jun 26 1996

Other

OtherProceedings of the 1996 4th IEEE International Workshop on Cellular Neural Networks, and Their Applications, CNNA-96
CitySeville, Spain
Period6/24/966/26/96

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ASJC Scopus subject areas

  • Software

Cite this

Foldesy, P., Zarandy, A., Szolgay, P., & Sziranyi, T. (1996). Real-life application case studies using CMOS 0.8μm CNN universal chip: Analogic algorithm for motion detection and texture segmentation. 363-368. Paper presented at Proceedings of the 1996 4th IEEE International Workshop on Cellular Neural Networks, and Their Applications, CNNA-96, Seville, Spain, .