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Proceedings Paper

Visual-tracking-based robot vision system
Author(s): Keqiang Deng; Joseph N. Wilson; Gerhard X. Ritter
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Paper Abstract

There are two kinds of depth perception for robot vision systems: quantitative and qualitative. The first one can be used to reconstruct the visible surfaces numerically while the second to describe the visible surfaces qualitatively. In this paper, we present a qualitative vision system suitable for intelligent robots. The goal of such a system is to perceive depth information qualitatively using monocular 2-D images. We first establish a set of propositions relating depth information, such as 3-D orientation and distance, to the changes of image region caused by camera motion. We then introduce an approximation-based visual tracking system. Given an object, the tracking system tracks its image while moving the camera in a way dependent upon the particular depth property to be perceived. Checking the data generated by the tracking system with our propositions provides us the depth information about the object. The visual tracking system can track image regions in real-time even as implemented on a PC AT clone machine, and mobile robots can naturally provide the inputs to our visual tracking system, therefore, we are able to construct a real-time, cost effective, monocular, qualitative and 3-dimensional robot vision system. To verify our idea, we present examples of perception of planar surface orientation, distance, size, dimensionality and convexity/concavity.

Paper Details

Date Published: 1 November 1992
PDF: 12 pages
Proc. SPIE 1826, Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods, (1 November 1992); doi: 10.1117/12.131621
Show Author Affiliations
Keqiang Deng, Univ. of Florida (United States)
Joseph N. Wilson, Univ. of Florida (United States)
Gerhard X. Ritter, Univ. of Florida (United States)

Published in SPIE Proceedings Vol. 1826:
Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods
David P. Casasent, Editor(s)

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