• DocumentCode
    553955
  • Title

    A method for customizing 3D virtual human body models based on Multi-class Support Vector Machine

  • Author

    Sun Yongjian ; Li Renwang ; Wan Changjiang ; Zhang Xiumei

  • Author_Institution
    Dept. of Ind. Eng., Zhejiang Sci-Tech Univ., Hangzhou, China
  • Volume
    1
  • fYear
    2011
  • fDate
    26-28 July 2011
  • Firstpage
    344
  • Lastpage
    347
  • Abstract
    How to generate a personalized 3D virtual body model conveniently and quickly is playing an increasingly important role in computer animation, virtual reality, entertainment, e-commerce and many other areas. Some related researchers just simply adjust human characteristic parameters to generate body model using existing 3D body model. In this article, in order to generate the personalized 3D virtual body model quickly, an approach based on Fuzzy Support Vector Machines (FSVM) is suggested. This constructs a classification model of the personalized body characteristic parameter. The one-versus-one (OVO) method based on the binary tree is used to handle a multiclass problem by breaking it into various two-class problems. Application of the method shows that the method of FSVM has the characteristics of less calculation and less error in the allowed range than the classical neural network.
  • Keywords
    fuzzy set theory; pattern classification; solid modelling; support vector machines; trees (mathematics); virtual reality; binary tree; classification; computer animation; entertainment; fuzzy support vector machines; one-versus-one method; personalized 3D virtual human body; virtual reality; Biological system modeling; Computational modeling; Humans; Kernel; Solid modeling; Support vector machines; Three dimensional displays; 3D human model; Customization; Human-centric products; Model customizing; Multi-class support vector machine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation (ICNC), 2011 Seventh International Conference on
  • Conference_Location
    Shanghai
  • ISSN
    2157-9555
  • Print_ISBN
    978-1-4244-9950-2
  • Type

    conf

  • DOI
    10.1109/ICNC.2011.6022036
  • Filename
    6022036