Classifying 3D Human Motions by Mixing Fuzzy Gaussian Inference with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  title =        "Classifying {3D} Human Motions by Mixing Fuzzy
                 Gaussian Inference with Genetic Programming",
  author =       "Mehdi Khoury and Honghai Liu",
  booktitle =    "Second International Conference on Intelligent
                 Robotics and Applications, ICIRA 2009",
  editor =       "Ming Xie and Youlun Xiong and Caihua Xiong and 
                 Honghai Liu and Zhencheng Hu",
  year =         "2009",
  volume =       "5928",
  series =       "Lecture Notes in Computer Science",
  pages =        "55--66",
  address =      "Singapore",
  month =        dec # " 16-18",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-10816-7",
  DOI =          "doi:10.1007/978-3-642-10817-4_6",
  bibdate =      "2009-12-18",
  bibsource =    "DBLP,
  abstract =     "This paper combines the novel concept of Fuzzy
                 Gaussian Inference(FGI) with Genetic Programming (GP)
                 in order to accurately classify real natural 3d human
                 Motion Capture data. FGI builds Fuzzy Membership
                 Functions that map to hidden Probability Distributions
                 underlying human motions, providing a suitable
                 modelling paradigm for such noisy data. Genetic
                 Programming (GP) is used to make a time dependent and
                 context aware filter that improves the qualitative
                 output of the classifier. Results show that FGI
                 outperforms a GMM-based classifier when recognizing
                 seven different boxing stances simultaneously, and that
                 the addition of the GP based filter improves the
                 accuracy of the FGI classifier significantly.",

Genetic Programming entries for Mehdi Khoury Honghai Liu