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Proceedings Paper

Neural Network Approach To Stereo Matching
Author(s): Y. T. Zhou; R. Chellappa
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Paper Abstract

A method for matching stereo images using a neural network is presented. We first fit a polynomial to find a smooth continuous intensity function in a window and estimate the first order intensity derivatives. Combination of smoothing and differentiation results in a window operator which functions very similar to the human eye in detecting the intensity changes. Since natural stereo images are usually digitized for the implementation on a digital computer, we consider the effect of spatial quantization on the estimation of the derivatives from natural images. A neural network is then employed for matching the estimated first order derivatives under the epipolar, photometric and smoothness constraints. Owing to the dense intensity derivatives a dense array of disparities is generated with only a few iterations. This method does not require surface interpolation. Experimental results using natural images pairs are presented to demonstrate the efficacy of our method.

Paper Details

Date Published: 16 December 1988
PDF: 8 pages
Proc. SPIE 0974, Applications of Digital Image Processing XI, (16 December 1988); doi: 10.1117/12.948464
Show Author Affiliations
Y. T. Zhou, University of Southern California (United States)
R. Chellappa, University of Southern California (United States)

Published in SPIE Proceedings Vol. 0974:
Applications of Digital Image Processing XI
Andrew G. Tescher, Editor(s)

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