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Proceedings Paper

Toward a pyramidal neural network system for stereo fusion
Author(s): Richard Lepage; Denis Poussart
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Paper Abstract

A goal of computer vision is the construction of scene descriptions based on information extracted from one or more 2-D images. Stereo is one of the strategies used to recover 3-D information from two images. Intensity edges in the images correspond mostly to characteristic features in the 3-D scene and the stereo module attempt to match corresponding features in the two images. Edge detection makes explicit important information about the two-dimensional image but is scale-dependent: edges are visible only over a range of scales. One needs multiple scale analysis of the input image in order to have a complete description of the edges. We propose a compact pyramidal architecture for image representation at multiple spatial scales. A simple Processing Element (PE) is allocated at each pixel location at each level of the pyramid. A dense network of weighted links between each PE and PEs underneath is programmed to generate the levels of the pyramid. Lateral weighted links within a level compute edge localization and intensity gradient. Feedback between successive levels is used to reinforce and refine the position of true edges. A fusion channel matches the two edge channels to output a disparity map of the observed scene.

Paper Details

Date Published: 1 March 1992
PDF: 12 pages
Proc. SPIE 1608, Intelligent Robots and Computer Vision X: Neural, Biological, and 3-D Methods, (1 March 1992); doi: 10.1117/12.135126
Show Author Affiliations
Richard Lepage, Univ. Laval (Canada)
Denis Poussart, Univ. Laval (Canada)

Published in SPIE Proceedings Vol. 1608:
Intelligent Robots and Computer Vision X: Neural, Biological, and 3-D Methods
David P. Casasent, Editor(s)

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