• DocumentCode
    2170810
  • Title

    Machine learning for computer graphics: a manifesto and tutorial

  • Author

    Hertzmann, Aaron

  • Author_Institution
    Toronto Univ., Ont., Canada
  • fYear
    2003
  • fDate
    8-10 Oct. 2003
  • Firstpage
    22
  • Lastpage
    36
  • Abstract
    It is argued that computer graphics can benefit from a deeper use of machine learning techniques. The author gives an overview of what learning has to offer the graphics community, with an emphasis on Bayesian techniques. He also attempts to address some misconceptions about learning, and to give a very brief tutorial on Bayesian reasoning.
  • Keywords
    Bayes methods; computer graphics; inference mechanisms; learning (artificial intelligence); Bayesian reasoning; Bayesian techniques; computer graphics; machine learning; Algorithm design and analysis; Animation; Art; Bayesian methods; Computer graphics; Machine learning; Machine learning algorithms; Rendering (computer graphics); Tutorial; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Graphics and Applications, 2003. Proceedings. 11th Pacific Conference on
  • Print_ISBN
    0-7695-2028-6
  • Type

    conf

  • DOI
    10.1109/PCCGA.2003.1238242
  • Filename
    1238242
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2170810