Share Email Print

Proceedings Paper

Stereo algorithm to reduce quantization noise effects in alarm systems
Author(s): Kevin W. J. Findlay; David Renshaw; Peter B. Denyer
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Over the years a considerable amount of research has been conducted in the area of passive stereo vision. Usually attempts have been made to solve the stereo correspondence problem in its most general sense and build an all purpose stereo module. Possible matches are proposed for all parts or edges of the image. The above general approach is not always necessary. Indeed there is evidence that the human vision system only attempts to match a small number of possible edges in a particular scene. In this paper we describe a computationally simple algorithm which takes advantage of the nature of the object being tracked. Disparity measurements are made for the entire edge and statistics used to provide subpixel accuracy. This approach reduces the problems caused by quantization noise when attempts are made to rectify the depth information. We show that stereo algorithms can be used and adapted in an application specific manner to construct viable systems in the areas of alarms and `invisible wall' detection. Results are presented to show the effectiveness of the algorithm in a number of both difficult and simple sequences. In conclusion, we believe our work demonstrates an industrially viable vision system requiring minimal hardware for implementation.

Paper Details

Date Published: 16 December 1992
PDF: 8 pages
Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130883
Show Author Affiliations
Kevin W. J. Findlay, Edinburgh Univ. (United Kingdom)
David Renshaw, Edinburgh Univ. (United Kingdom)
Peter B. Denyer, Edinburgh Univ. (United Kingdom)

Published in SPIE Proceedings Vol. 1766:
Neural and Stochastic Methods in Image and Signal Processing
Su-Shing Chen, Editor(s)

© SPIE. Terms of Use
Back to Top