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

Author

Földesy, Péter ; Zarándy, Ákos ; Szolgay, Péter ; Szirányi, Tamás

Author_Institution

Comput. & Autom. Inst., Hungarian Acad. of Sci., Budapest, Hungary

fYear

1996

Firstpage

363

Lastpage

368

Abstract

Using a 20×22 CNN Universal Machine chip two application case studies are presented. A new analogic CNN algorithm is 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 is given next with approximately 2 μs processing speed and with less than 5% misclassification error rate for 4 natural textures in a real-life testing environment

Keywords

CMOS analogue integrated circuits; cellular neural nets; image classification; image segmentation; image sequences; image texture; motion estimation; neural chips; object detection; 0.8 micron; 17 mus; 20×22 CNN Universal Machine chip; CMOS 0.8 μm CNN Universal chip; analogic algorithm; black-and-white image sequences; misclassification error rate; motion detection; natural textures; texture classification; texture segmentation; Cellular neural networks; Computer aided software engineering; Logic; Motion detection; Prototypes; Registers; Switches; Testing; Thyristors; Turing machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Cellular Neural Networks and their Applications, 1996. CNNA-96. Proceedings., 1996 Fourth IEEE International Workshop on

Conference_Location

Seville

Print_ISBN

0-7803-3261-X

Type

conf

DOI

10.1109/CNNA.1996.566601

Filename

566601