Title :
Machine learning for computer graphics: a manifesto and tutorial
Author :
Hertzmann, Aaron
Author_Institution :
Toronto Univ., Ont., Canada
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;
Conference_Titel :
Computer Graphics and Applications, 2003. Proceedings. 11th Pacific Conference on
Print_ISBN :
0-7695-2028-6
DOI :
10.1109/PCCGA.2003.1238242