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Segmentation of bottom shadow of vehicle based on improved PSO-MBCV algorithm

  • Beijing Institute of Technology
  • Ministry of Education in China

科研成果: 期刊稿件文章同行评审

摘要

The current segmentation algorithms of bottom shadow of vehicle have poor robustness, meanwhile, the multilevel thresholds segmentation algorithm of maximum between-class variance (MBCV) method does not determine automatically the number of the thresholds. Therefore, firstly, the peak adaptive method based on image histogram is used to determine the number of thresholds; then, the number is considered as the particle dimension of the particle swarm optimization (PSO) algorithm, and the bottom shadow of vehicles based on an improved PSO-MBCV algorithm is proposed. The results show that the misclassification error (ME) can be deduced and the bottom shadow of vehicles can be effectively segmented.

源语言英语
页(从-至)1439-1445
页数7
期刊Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics
36
7
DOI
出版状态已出版 - 7月 2014

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