• DocumentCode
    353352
  • Title

    Mesh construction with fast soft vector quantisation

  • Author

    Borghese, N. Alberto ; Ferrari, Stefano

  • Author_Institution
    Lab. of Human Motion Anal. & Virtual Reality, CNR, Milano, Italy
  • Volume
    5
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    473
  • Abstract
    In this paper a method to accelerate soft vector quantisation (VQ), making it a quasi-real time procedure, is described. Through the local analysis of the data density a criterion to set a reasonable value of the parameters and to initialise the position of the reference vectors (hyper-box preprocessing), allows to cut about 75% of the iterations and to make the computational cost of each iteration constant, independent of the number of sampled points. Moreover, it makes soft VQ of possible implementation on parallel machines. Overall the processing time with hyper-box pre-processing can be brought down to 3%. This method, in conjunction with Delaunay tessellation, has been extensively applied to the construction of 3D triangular meshes from dense noisy data. Results on the reconstruction of 3D models of human faces are reported and discussed
  • Keywords
    computational complexity; mesh generation; neural nets; noise; vector quantisation; 3D model reconstruction; 3D triangular meshes; Delaunay tessellation; computational cost; dense noisy data; fast soft vector quantisation; human faces; hyper-box preprocessing; mesh construction; neural nets; parallel implementation; quasi-real-time procedure; reference vector position initialisation; soft VQ; Acceleration; Annealing; Computational efficiency; Data analysis; Humans; Image reconstruction; Laboratories; Motion analysis; Vector quantization; Virtual reality;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
  • Conference_Location
    Como
  • ISSN
    1098-7576
  • Print_ISBN
    0-7695-0619-4
  • Type

    conf

  • DOI
    10.1109/IJCNN.2000.861514
  • Filename
    861514