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Proceedings Paper

Matching of road segments using probabilistic relaxation: reducing the computational requirements
Author(s): William J. Christmas; Josef Kittler; Maria Petrou
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Paper Abstract

In previous work we presented an algorithm for matching features extracted from an image with those extracted from a model, using a probabilistic relaxation method. Because the algorithm compares each possible match with all other possible matches, the main obstacle to its use on large data sets is that both the computation time and the memory usage are proportional to the square of the number of possible matches. This paper describes some improvements to the algorithm to alleviate these problems. The key sections of the algorithm are the generation, storage, and use of the compatibility coefficients. We describe three different schemes that reduce the number of these coefficients. The execution time is improved in each case, even when the number of iterations required for convergence is greater than in the unmodified algorithm. We show that the new methods also perform well, generating good matches in all cases.

Paper Details

Date Published: 13 July 1994
PDF: 11 pages
Proc. SPIE 2220, Sensing, Imaging, and Vision for Control and Guidance of Aerospace Vehicles, (13 July 1994); doi: 10.1117/12.179601
Show Author Affiliations
William J. Christmas, Univ. of Surrey (United Kingdom)
Josef Kittler, Univ. of Surrey (United Kingdom)
Maria Petrou, Univ. of Surrey (United Kingdom)

Published in SPIE Proceedings Vol. 2220:
Sensing, Imaging, and Vision for Control and Guidance of Aerospace Vehicles
Jacques G. Verly; Sharon S. Welch, Editor(s)

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