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Proceedings Paper

Depth perception using crossed-looking stereo vision system
Author(s): Chih-Ping Yeh
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Paper Abstract

It has been well known that the human vision system is a multi-resolution and spatially shift- variant system. The size of the edge filters on each retina are small and roughly constant within the foveal area, and are increased linearly with eccentricity outside the fovea. This mechanism allows the human visual system to perceive detailed description about the target surface within the fovea vision area, and to obtain a global description about the scene in the peripheral vision area. This paper describes a stereo vision system which simulates this mechanism of human vision. The system uses a pair of crossed-looking cameras as sensors. The fixation point is used as the geometric center of the 3D space. With this perception geometry, the vision system perceives depth variation of the target surface, rather than the absolute distance from the target surface to the sensors. This property allows the stereo correspondence to be achieved through a fusion process, which is similar to the optical fusion of two diffraction patterns. The fusion process obtains disparity information of the entire image in one convolution operation. The volume of calculation, and therefore the processing time needs for depth perception, is largely reduced. The mechanism of spatially shift-variant processing is implemented by applying logarithm conformal mapping to the images. As a result, the sensitivity in depth perception decreases exponentially from the center to the peripheral area of the image. This allows the vision system to obtain depth information about the scene within a broad field of view.

Paper Details

Date Published: 13 October 1994
PDF: 11 pages
Proc. SPIE 2354, Intelligent Robots and Computer Vision XIII: 3D Vision, Product Inspection, and Active Vision, (13 October 1994); doi: 10.1117/12.189105
Show Author Affiliations
Chih-Ping Yeh, Wayne State Univ. (United States)

Published in SPIE Proceedings Vol. 2354:
Intelligent Robots and Computer Vision XIII: 3D Vision, Product Inspection, and Active Vision
David P. Casasent, Editor(s)

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