Abstract
The paper proposes a novel feature fusion concept for object extraction. The image feature extraction process is modeled as a feature detection problem in noise. The geometric features are probabilistically modeled and detected under various detection thresholds. These detection results are then fused within the Bayesian framework to obtain the final features for further processing. Along with a probabilistic model, pixels voting algorithm is also tested through binary threshold variation. The performance of these approaches is compared with the traditional approaches of image feature extraction in the context of automatic license plate detection problem.
Original language | English |
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Pages | 398-403 |
Number of pages | 6 |
Publication status | Published - 2007 |
Event | 2nd International Conference on Computer Vision Theory and Applications, VISAPP 2007 - Barcelona, Spain Duration: Mar 8 2007 → Mar 11 2007 |
Conference
Conference | 2nd International Conference on Computer Vision Theory and Applications, VISAPP 2007 |
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Country/Territory | Spain |
City | Barcelona |
Period | 3/8/07 → 3/11/07 |
Keywords
- Data fusion
- Extraction
- Location
- LPR
- Plate
ASJC Scopus subject areas
- Computer Science Applications
- Computer Vision and Pattern Recognition
- Software