Journal of South China University of Technology (Natural Science Edition) ›› 2018, Vol. 46 ›› Issue (1): 131-138，144.doi: 10.3969/j.issn.1000-565X.2018.01.017

• Computer Science & Technology • Previous Articles     Next Articles

High Accurate Contour Vision Algorithm Based on Shape Context and ICP

LIU Yu1 SUN Kun1 XIE Hongwei2    

  1. 1． School of Automation Science and Engineering，South China University of Technology，Guangzhou 510640，Guangdong，China;
    2． School of Mechanical and Electrical Engineering，Guangzhou University，Guangzhou 510006，Guangdong，China)

  • Received:2017-03-26 Revised:2017-06-25 Online:2018-01-25 Published:2017-12-01
  • Contact: 谢宏威( 1981-) ，男，副研究员，主要从事机器视觉及其相关应用研究工作 E-mail:xhw_cn@foxmail.com
  • About author:刘屿( 1977) ，男，副研究员，主要从事分布参数系统控制、视觉检测研究
  • Supported by:
    The Natural Science Foundation of Guangdong Province of China( 2015A030310308) and the Science and Technology Planning Project of Guangdong Province ( 2017B090910006，2016B010126001，2016B090927010， 2016A010102021，2015B090901049) 

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Abstract: In order to solve the problems of high precision contour measurement with arbitrary shape, a high accurate vision algorithm is proposed based on shape context descriptor and iterative closest point (ICP). Firstly, the sub-pixel edge of workpiece is extracted by using edge extraction algorithm based on local area, then the noise is filtered out and contour is completed. Secondly, based on coarse-to-fine matching strategy, the coarse matching is performed by using shape context descriptor and the fine matching is performed by using iterative closest point for obtaining the sub-pixel accuracy contour matching. Finally, the neighbor-method is proposed to calculate the contour errors. The experimental results show that the detection accuracy of the proposed algorithm can reach to 0.5 pixel, which can meet the needs of practical application.

Key words: contour measurement, shape context, iterative point clouds, sub-pixel edge detection

CLC Number: