吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (增刊1): 384-388.

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Unstructured road segmentation method based on dictionary learning and sparse representation

XIAO Liang, DAI Bin, WU Tao, FANG Yu-qiang   

  1. College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha 410072, China
  • Received:2012-09-30 Published:2013-06-01

Abstract:

For vision navigation of ALV in complicated environments,a novel dictionary learning and sparse representation based road segmentation algorithm was proposed.The local image patch was used as the processing unit;a dictionary was learned based on man-selected typical road image and the dictionary could be updated in real time by online dictionary learning with the little piece of image right before the vehicle as supervision.With this dictionary,the on-road patches could be sparse represented precisely while the off-road patches could not A dictionary Learning and sparse representation based on classification framework was built and the local image patches could be classified by the reconstruction errors of sparse representation.A variety of experiments show that the proposed algorithm is suitable for various unstructured environments and is robust to illumination,shadow and water stains;unstructured environment.

Key words: dictionary learning, sparse representation, road segmentation, unstructured environment

CLC Number: 

  • TP391

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