吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (3): 890-896.doi: 10.13229/j.cnki.jdxbgxb201603032

• Orginal Article • Previous Articles     Next Articles

Twist-lock online recognition based on improved incremental PCA by Kinect

MA Shuang1, 2, ZHOU Chang-jiu2, ZHANG Lian-dong2, HONG Wei1, TIAN Yan-tao1   

  1. 1.College of Communication Engineering, Jilin University, Changchun 130022, China;
    2.Advanced Robotics and Intelligent Control Centre, Singapore Polytechnic, Singapore 139651,Singapore
  • Received:2014-11-26 Online:2016-06-20 Published:2016-06-20

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Abstract: Research of the cognitive recognition of twist-lock automation handling system is conducted. In this research, Kinect is employed to collect environment and objects information, and an improved incremental Principal Component Analysis (PCA) is proposed to build real-time cognitive recognition system. In online learning phase, the new class is monitored and feature vectors are updated incrementally based on the difference between the new input and the reconstruction one using current eigenvectors; the feature vectors are optimized and the inner-class distance threshold is updated adaptively based on comparison of inner-class distance. Thereby, the proposed algorithm can convert high-dimension information to low-dimension machine expression, learn, update and accumulate feature knowledge online, and complete pattern recognition task at the same time. Experiment results show that the proposed algorithm can improve the adaptability, robustness, recognition rate and real-time performance of a visual system, Moreover, calculation and storage space can be reduced by controlling the feature space dimension.

Key words: computer application, online learning, incremental principal component analysis(PCA), adaptive feature update, cognitive recognition

CLC Number: 

  • TP391.4
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