Maximum likelihood estimates for object detection using multiple detectors
Oskarsson, Magnus LU
and Åström, Karl
LU
(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:
https://lup.lub.lu.se/record/389927
- author
- Oskarsson, Magnus
LU
and Åström, Karl
LU
- organization
- publishing date
- 2006
- 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
- 2024-01-07 17:04:36
@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}},
}