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Proceedings Paper

A Connectionist Architecture For Matching 3-D Models To Moving Edge Features
Author(s): David Gungner; Josef Skrzypek
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Paper Abstract

Autonomous mobile robots must be capable of real-time recognition of moving objects in a time-varying scene. The motion of rigid objects can be analyzed in terms of edge features undergo-ing translation and rotation in the two-dimensional image. These features could be manipulated to match a fixed 3-D model. However, in a goal-driven system, where a priori knowledge of the 3-D model exists, it is more natural to manipulate the model before matching it to the edge features. We could speed up the 3-D to 2-D matching process by precomputing a range of possible orientations for the 3-D model. The difficulty is that one cannot predict all of the possible global changes of an object as it is moving in the scene. Our solution is to constrain the problem by looking at local changes that result from moving edge-features. The concept is borrowed from natural vision, where the visual field is divided into small regions and each region is analyzed by a column of orientation sensitive line segment operators. Each orientation operator has a preset maximal sensitivity to lines of a specific angle. Initially we assume that the 3-D model of a moving object has already been given. As an object rotates in the scene, the result is a spatial sequence of edges that activates adjacent columns. Heuristics to speed-up the matching of a 3-D model to a rotating or translating object can be derived from the local communication between neighboring columns of the moving edge features. The distance between successively activated columns can be used to measure how smoothly the edge features are undergoing motion. A slow moving object will produce a response in only a few successively adjacent columns and be interpreted as stepping slowly across the scene. A rapidly translating or rotating object will activate widely separated columns and be processed as undergoing discontinuous jumps. Similarly, in humans, when the velocity of key features in the visual field are either too low or too high, objects are perceived to be jumping instead of undergoing smooth motion [1].

Paper Details

Date Published: 27 March 1987
PDF: 6 pages
Proc. SPIE 0726, Intelligent Robots and Computer Vision V, (27 March 1987); doi: 10.1117/12.937734
Show Author Affiliations
David Gungner, University of California at Los Angeles (United States)
Josef Skrzypek, University of California at Los Angeles (United States)


Published in SPIE Proceedings Vol. 0726:
Intelligent Robots and Computer Vision V
David P. Casasent, Editor(s)

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