Incorporating negentropy in saliency-based search free car number plate localization

Safaei, A. ORCID: 0000-0003-4696-8968, Tang, H.L. and Sanei, S. ORCID: 0000-0002-3437-2801, 2016. Incorporating negentropy in saliency-based search free car number plate localization. In: 2016 IEEE International Conference on Digital Signal Processing (DSP 2016), Beijing, China, 16-18 October 2016. Piscataway, N.J.: Institute of Electrical and Electronics Engineers, pp. 667-671. ISBN 9781509041657

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Abstract

License plate localization algorithms aim to detect license plates within the scene. In this paper, a new algorithm is discussed where the necessary conditions are imposed into the saliency detection equations. Measures of distance between probability distributions such as negentropy finds the candidate license plates in the image and the Bayesian methodology exploits the a priori information to estimate the highest probability for each candidate. The proposed algorithm has been tested for three datasets, consisting of gray-scale and color images. A detection accuracy of 96% and an average execution time of 80 ms for the first dataset are the marked outcomes. The proposed method outperforms most of the state-of-the-art techniques and it is suitable to use in real-time ALPR applications.

Item Type: Chapter in book
Creators: Safaei, A., Tang, H.L. and Sanei, S.
Publisher: Institute of Electrical and Electronics Engineers
Place of Publication: Piscataway, N.J.
Date: 2016
ISSN: 2165-3577
Identifiers:
NumberType
10.1109/ICDSP.2016.7868642DOI
Divisions: Schools > School of Science and Technology
Depositing User: Jonathan Gallacher
Date Added: 05 Sep 2018 15:55
Last Modified: 05 Sep 2018 15:55
URI: http://irep.ntu.ac.uk/id/eprint/34435

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