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Proceedings Paper

Road recognition algorithm using principal component neural networks and K-means
Author(s): Hong Cheng; Nanning Zheng; Qing Ling; Zhenhai Gao
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Paper Abstract

A new road recognition algorithm based on local statistical features and principal component analysis is introduced to improve whose robustness and adaptiveness. The weights of the principal component neural networks is trained with the aid of the algorithm of generalized Hebbian learning rule, and the input vectors of the local spatial features and image pixels value are transformed into feature vectors which are once clustered by K-means classifier, the road surface and un-road surface can be distinguished by the reference area finally. The simulation results confirm the fine robustness and adaptiveness of the newly proposed algorithm, especially, the improved performance to recognize road images affected by illumination variations or shadows.

Paper Details

Date Published: 25 September 2003
PDF: 4 pages
Proc. SPIE 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition, (25 September 2003); doi: 10.1117/12.538796
Show Author Affiliations
Hong Cheng, Xi'an Jiaotong Univ. (China)
Nanning Zheng, Xi'an Jiaotong Univ. (China)
Qing Ling, Xi'an Jiaotong Univ. (China)
Zhenhai Gao, Xi'an Jiaotong Univ. (China)

Published in SPIE Proceedings Vol. 5286:
Third International Symposium on Multispectral Image Processing and Pattern Recognition
Hanqing Lu; Tianxu Zhang, Editor(s)

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