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Lane mark segmentation method based on maximum entropy

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5 Author(s)
Yu Tianhong ; Coll. of Transp., Jilin Univ., Changchun, China ; Wang Rongben ; Jin Lisheng ; Chu Jiangwei
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In order to realize lane mark identifying and tracking on such conditions as uneven road surface materials and different illumination etc, this paper proposes a new method which combines an image segmentation technique based on maximum entropy with a bi-normalized adjustable template. First, applying image window variation technology, this method first realizes the better road image segmentation based on maximize one-dimension entropy. Second, lane mark parameters can be acquired based on the bi-normalized adjustable template. Finally lane mark real-time tracking is realized by applying trapezia AOI method.

Published in:

Intelligent Transportation Systems, 2005. Proceedings. 2005 IEEE

Date of Conference:

13-15 Sept. 2005