Probabilistic modeling and fusion for image feature extraction with applications to license plate detection

Rami Al-Hmouz*, Subhash Challa, Duc Vo

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

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 languageEnglish
Pages398-403
Number of pages6
Publication statusPublished - 2007
Externally publishedYes
Event2nd International Conference on Computer Vision Theory and Applications, VISAPP 2007 - Barcelona, Spain
Duration: Mar 8 2007Mar 11 2007

Conference

Conference2nd International Conference on Computer Vision Theory and Applications, VISAPP 2007
Country/TerritorySpain
CityBarcelona
Period3/8/073/11/07

Keywords

  • Data fusion
  • Extraction
  • Location
  • LPR
  • Plate

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Software

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