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Proceedings Paper

Multiresolution neural network for the fusion of stereo and shape from shading in machine vision
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 2D images. A reconstruction strategy based on a three-level representational framework is proposed. The first representational level, the Primal Sketch, makes explicit important information about the two-dimensional image, primarily the intensity changes and their geometrical distribution and organization. The intensity changes appear at several spatial scales and image analysis performed at multiple resolutions is therefore required. We propose a compact pyramidal neural network implementation of the multiresolution representation of the input images. Features of the scene are detected at each resolution level and feedback interaction is built between pyramid levels in order to reinforce edges which correspond to physical features of the observed scene. The second representational level, the raw 2.5 D Sketch, makes explicit the orientation and rough depth at the edge location of the visible surfaces. A multiresolution neural network stereo algorithm is designed to compute the disparity at each pixel location and at all the resolution levels. Matching is facilitated by a hierarchical focussing mechanism. The third representational level, the full 2.5 D Sketch, makes explicit the orientation and depth estimate at all the visible surface coordinates. Depth information between the edges is computed with a local shape- from-shading algorithm.

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: 10.1117/12.131604
Show Author Affiliations
Richard Lepage, Univ. Laval (Canada)
Denis Poussart, Univ. Laval (Canada)

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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