License Plate Localization With Efficient Markov Chain Monte Carlo
Authors: 
Lijun Cao, Xu Zhang, Weihua Chen, Kaiqi HuangAuthors Info & Claims
Lijun Cao, Xu Zhang, Weihua Chen, Kaiqi HuangAuthors Info & ClaimsPages 295 - 298
Abstract
This paper presents a novel efficient Markov Chain Monte Carlo (MCMC) method for License Plate (LP) localization. The proposed method formulates the LP image feature and prior knowledge into a unified Bayesian framework. Then the localization problem is derived as a maximizing-a-posterior (MAP) problem, which integrates color, edge and character feature of LP. We propose an efficient MCMC method, taking integrated local geometrical likelihood as proposal probability to make the inference feasible. The experimental results on real dataset are very promising in terms of detection rate and localization accuracy.
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Index Terms
- License Plate Localization With Efficient Markov Chain Monte Carlo
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Information & Contributors
Information
Published In
July 2014
430 pages
ISBN:9781450328104
DOI:10.1145/2632856
- General Chairs:
- Hanzi Wang,
- Larry Davis,
- Program Chairs:
- Wenwu Zhu,
- Stephan Kopf,
- Yanyun Qu,
- Publications Chairs:
- Jun Yu,
- Jitao Sang,
- Tao Mei
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In-Cooperation
- NSF of China: National Natural Science Foundation of China
- Beijing ACM SIGMM Chapter
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 10 July 2014
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ICIMCS '14
ICIMCS '14: International Conference on Internet Multimedia Computing and Service
July 10 - 12, 2014
Xiamen, China
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Overall Acceptance Rate 163 of 456 submissions, 36%
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