DocumentCode
3416329
Title
Retinomorphic vision systems
Author
Boahen, Kwabena
Author_Institution
California Inst. of Technol., Pasadena, CA, USA
fYear
1996
fDate
12-14 Feb 1996
Firstpage
2
Lastpage
14
Abstract
The new generation of silicon retinae has two defining characteristics. First, these synthetic retinae are morphologically equivalent to their biological counterparts-at an appropriate level of abstraction. Second, they accomplish all four major operations performed by biological retinae using neurobiological principles: (1) continuous sensing for detection, (2) local automatic gain control for amplification, (3) spatiotemporal bandpass filtering for preprocessing, and (4) adaptive sampling for quantization. The author introduces the term retinomorphic to refer to this subclass of the neuromorphic electronic systems. Their design principles are compared and contrasted with the standard practice in imager design. It is argued that neurobiological principles are best suited to perceptive systems that go beyond reproducing the dynamic scene, like a conventional video camera does, to extracting salient information in real time. The results from a fully operational retinomorphic vision system are presented and the trade-offs involved in its design are discussed
Keywords
CMOS analogue integrated circuits; analogue processing circuits; automatic gain control; image processing; image processing equipment; image sensors; neural chips; adaptive sampling; amplification; automatic gain control; continuous sensing; design principles; imager design; local AGC; neurobiological principles; neuromorphic electronic systems; perceptive systems; preprocessing; quantization; real time processing; retinomorphic vision systems; silicon retina; spatiotemporal bandpass filtering; synthetic retinae; Adaptive control; Adaptive filters; Band pass filters; Character generation; Filtering; Gain control; Machine vision; Programmable control; Silicon; Spatiotemporal phenomena;
fLanguage
English
Publisher
ieee
Conference_Titel
Microelectronics for Neural Networks, 1996., Proceedings of Fifth International Conference on
Conference_Location
Lausanne
ISSN
1086-1947
Print_ISBN
0-8186-7373-7
Type
conf
DOI
10.1109/MNNFS.1996.493766
Filename
493766
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