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Published Articles >> Table of Contents >> Abstract
April 1996 (Vol. 18, No. 4)
pp. 438-443
A Deformable Template Approach to Detecting Straight Edges in Radar Images
Sridhar Lakshmanan
David Grimmer
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.491625
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| Abstract |
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This paper addresses the problem of locating two straight and parallel road edges in images that are acquired from a stationary millimeter-wave radar platform positioned near ground-level. A fast, robust, and completely data-driven Bayesian solution to this problem is developed, and it has applications in automotive vision enhancement. The method employed in this paper makes use of a deformable template model of the expected road edges, a two-parameter log-normal model of the ground-level millimeter-wave (GLEM) radar imaging process, a maximum a posteriori (MAP) formulation of the straight edge detection problem, and a Monte Carlo algorithm to maximize the posterior density. Experimental results are presented by applying the method on GLEM radar images of actual roads. The performance of the method is assessed against ground truth for a variety of road scenes.
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 Additional Information
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Index Terms- Road boundary detection, millimeter-wave images, global shape models, log-normal densities, Metropolis algorithm.
Citation:
Sridhar Lakshmanan, David Grimmer,
"A Deformable Template Approach to Detecting Straight Edges in Radar Images,"
IEEE Transactions on Pattern Analysis and Machine Intelligence,
vol. 18,
no. 4,
pp. 438-443,
Apr.,
1996
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