D2 shape distribution and artificial neural networks for 3D objects recognition

  • Authors

    • Safae Elhoufi
    • Aicha Majda
    • Khalid Abbad
    2018-04-15
    https://doi.org/10.14419/ijet.v7i2.13.11620
  • 3D Object, VRML, 3D/3D Indexing, Shape Distribution, Artificial Neural Network, Classification, 3D Retrieval, Recognition.

  • Abstract

    In this paper, we propose a 3D object recognition approach, based on the shape distribution D2 and artificial neural networks. The challenge is to discriminate between similar and dissimilar shapes by finding a shape signature that can be constructed and classified quickly. We propose a connectionist system to recognize 3D objects in VRML (Virtual Reality Modeling Language) format. The key idea is to represent the signature of an object as a shape distribution sampled from a shape function measuring global geometric properties of an object. The proposed strategy is the following: from a polygon object to be recognized, a triangulation is performed. Then, distances are calculated between two random points of the triangulated surface of the 3D object. The frequency of these distances will be represented by a normalized histogram. The values of these histograms feed a multi-layer neural network with back- propagation training. We demonstrate the potential of this approach in a set of experiments, which proved our system could achieve above 91.7% recognition rate. In addition, to evaluate the efficiency of our method, we compare our classifier with Support vector machine and k- nearest neighbours. The simulation results highlight the performance of the proposed approach.

     

     

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  • How to Cite

    Elhoufi, S., Majda, A., & Abbad, K. (2018). D2 shape distribution and artificial neural networks for 3D objects recognition. International Journal of Engineering & Technology, 7(2.13), 103-108. https://doi.org/10.14419/ijet.v7i2.13.11620

    Received date: 2018-04-16

    Accepted date: 2018-04-16

    Published date: 2018-04-15