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Maximum likelihood estimates for object detection using multiple detectors

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Author

Summary, in English

Object detection in real images has attracted much attention during the last decade. Using machine learning and large databases it is possible to develop detectors for visual categories that have a very high hit-rate, with low false positive rates. In this paper we investigate a general probabilistic framework for context based scene interpretation using multiple detectors. Methods for finding maximum likelihood estimates of scenes given detection results are presented. Although we have investigated how the method works for a specific case, namely for face detection, it is a general method. We show how to combine the results of a number of detectors i.e. face, eye, nose and mouth detectors. The methods have been tested using detectors trained on real images, with promising results.

Publishing year

2006

Language

English

Pages

658-666

Publication/Series

Structural, Syntactic, and Statistical Pattern Recognition, Proceedings (

Volume

4109

Document type

Conference paper

Publisher

Springer

Topic

  • Mathematics

Conference name

Joint IAPR International Workshops, SSPR 2006 and SPR 2006

Conference date

2006-08-17 - 2006-08-19

Conference place

Hong Kong, China

Status

Published

ISBN/ISSN/Other

  • ISSN: 0302-9743
  • ISSN: 1611-3349
  • ISBN: 978-3-540-37236-3