The Community for Technology Leaders
RSS Icon
Subscribe
Kokubunji, Tokyo, Japan
Oct. 26, 2004 to Oct. 29, 2004
ISBN: 0-7695-2187-8
pp: 20-25
Balaji Krishnapuram , Microsoft Research
Christopher M. Bishop , Microsoft Research
Martin Szummer , Microsoft Research
ABSTRACT
Recognition of hand-drawn shapes is an important and widely studied problem. By adopting a generative probabilistic framework we are able to formulate a robust and flexible approach to shape recognition which allows for a wide range of shapes and which can recognize new shapes from a single exemplar. It also provides meaningful probabilistic measures of model score which can be used as part of a larger probabilistic framework for interpreting a page of ink. We also show how Bayesian model comparison allows the trade-off between data fit and model complexity to be optimized automatically.
INDEX TERMS
null
CITATION
Balaji Krishnapuram, Christopher M. Bishop, Martin Szummer, "Generative Models and Bayesian Model Comparison for Shape Recognition", IWFHR, 2004, Proceedings. Ninth International Workshop on Frontiers in Handwriting Recognition, Proceedings. Ninth International Workshop on Frontiers in Handwriting Recognition 2004, pp. 20-25, doi:10.1109/IWFHR.2004.46
19 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool