Map-based probabilistic reasoning to vehicle segmentation

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This paper proposes a segmentation algorithm by means of a probabilistic reasoning to segment moving vehicles in front of a moving vehicle in a road traffic scene. According to the perceptually known facts of a target, we extract image primitives and update a probabilistic expectation for the target to be in an image. Since a noise image produces unreliable features and degrades the detection and localization, selecting the image primitives, which are less sensitive to noise and represent the facts well, is important. The probabilistic reasoning overcomes this problem baased on MAP (maximum a posteriori) probability that combines the prior and likelihood probabilities of image features using Bayes' rule. (C) 1998 Published by Elsevier Science Ltd on behalf of the Pattern Recognition Society. All rights reserved.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
1998-12
Language
English
Article Type
Article
Keywords

MOTION SEGMENTATION; OBJECT RECOGNITION; IMAGE SEQUENCES; TRACKING; MODEL; SCENES

Citation

PATTERN RECOGNITION, v.31, no.12, pp.2017 - 2026

ISSN
0031-3203
DOI
10.1016/S0031-3203(98)00027-2
URI
http://hdl.handle.net/10203/73984
Appears in Collection
EE-Journal Papers(저널논문)
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