Velocity estimation and comparison of two insect-vision-based motion-detection models

Sreeja Rajesh; David C. O'Carroll; Derek Abbott

doi:10.1117/12.514887

14 October 2003 Velocity estimation and comparison of two insect-vision-based motion-detection models

Sreeja Rajesh, David C. O'Carroll, Derek Abbott

Proceedings Volume 5062, Smart Materials, Structures, and Systems; (2003) https://doi.org/10.1117/12.514887
Event: Smart Materials, Structures, and Systems, 2002, Bangalore, India

Abstract

Insects are blessed with a very efficient yet simple visual system which enable them to navigate with great ease and accuracy. Though a lot has been done in the field of insect vision, there is still not a clear understanding of how velocity is determined in biological vision systems. The dominant model for insect motion detection, first proposed by Hassentein and Reichardt in 1956 has gained widespread acceptance in the invertebrate vision community. The template model, proposed later by Horridge in 1990, permits simple tracking techniques and lends itself easily to both hardware and software. Analysis and simulation by Dror suggest that the inclusion of additional system components to perform pre-filtering, response compression, integration and adaptation, to a basic Reichardt correlator can make it less sensitive to contrast and spatial structure thereby providing a more robust estimate of local image velocity. It was found from the data obtained, from the intracellular recordings of the steady state responses of wide field neurons in the hoverfly Volucella, that the shape of the curves obtained, agreed perfectly with the theoretical predictions made by Dror. In order to compare it with the template model, an experiment was done to get the velocity response curves of the template model using the same image statistics. The results leads us to believe that the fly motion detector emulates a modified Reichardt correlator.

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Sreeja Rajesh, David C. O'Carroll, and Derek Abbott "Velocity estimation and comparison of two insect-vision-based motion-detection models", Proc. SPIE 5062, Smart Materials, Structures, and Systems, (14 October 2003); https://doi.org/10.1117/12.514887

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KEYWORDS

Motion models

Optical correlators

Neurons

Velocity measurements

Motion detection

Visual process modeling

Sensors

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