Paper
21 November 2001 Implementation of visual motion detection with contrast adaptation
Author Affiliations +
Proceedings Volume 4591, Electronics and Structures for MEMS II; (2001) https://doi.org/10.1117/12.449162
Event: International Symposium on Microelectronics and MEMS, 2001, Adelaide, Australia
Abstract
Visual detection and processing of motion in insects is thought to occur based on an elementary delay-and-correlate operation at an early stage in the visual pathway. The correlational elementary motion detector (EMD) indicates the presence of moving stimuli on the retina and is directionally sensitive, but it is a complex spatiotemporal filter and does not inherently encode important motion parameters such as velocity. However, additional processing, in combination with natural visual stimuli, may allow computation of useful motion parameters. One such feature is adaptation in response to motion, until recently thought to occur by modification of the delay time constant, but now shown to arise due mainly to adjustment of contrast gain. This adaptation renders EMD output less dependent on scene contrast and enables it to carry some velocity information. We describe an ongoing effort to characterize this system in engineering terms, and to implement an analog VLSI model of it. Building blocks for a correlational EMD, and a mechanism for computing and implementing adjustment of contrast gain are described. This circuitry is intended as front-end processing for classes of higher-level visual motion computation also performed by insects, including estimation of egomotion by optical flow, and detection of moving targets.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Patrick A. Shoemaker, David C. O'Carroll, and Andrew D. Straw "Implementation of visual motion detection with contrast adaptation", Proc. SPIE 4591, Electronics and Structures for MEMS II, (21 November 2001); https://doi.org/10.1117/12.449162
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Cited by 11 scholarly publications.
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KEYWORDS
Visualization

Linear filtering

Neurons

Motion models

Visual process modeling

Sensors

Analog electronics

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