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Maximum likelihood estimates for object detection using multiple detectors

Oskarsson, Magnus LU and Åström, Karl LU orcid (2006) Joint IAPR International Workshops, SSPR 2006 and SPR 2006 4109. p.658-666
Abstract
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Structural, Syntactic, and Statistical Pattern Recognition, Proceedings (
volume
4109
pages
658 - 666
publisher
Springer
conference name
Joint IAPR International Workshops, SSPR 2006 and SPR 2006
conference location
Hong Kong, China
conference dates
2006-08-17 - 2006-08-19
external identifiers
  • wos:000240075100072
  • scopus:33749610175
ISSN
1611-3349
0302-9743
ISBN
978-3-540-37236-3
DOI
10.1007/11815921_72
language
English
LU publication?
yes
id
103a584e-0f14-49fd-8529-dba12e8abad3 (old id 389927)
date added to LUP
2016-04-01 11:41:50
date last changed
2021-06-30 02:19:44
@inproceedings{103a584e-0f14-49fd-8529-dba12e8abad3,
  abstract     = {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.},
  author       = {Oskarsson, Magnus and Åström, Karl},
  booktitle    = {Structural, Syntactic, and Statistical Pattern Recognition, Proceedings (},
  isbn         = {978-3-540-37236-3},
  issn         = {1611-3349},
  language     = {eng},
  pages        = {658--666},
  publisher    = {Springer},
  title        = {Maximum likelihood estimates for object detection using multiple detectors},
  url          = {http://dx.doi.org/10.1007/11815921_72},
  doi          = {10.1007/11815921_72},
  volume       = {4109},
  year         = {2006},
}