An adaptive quantum-behaved particle swarm optimization approach for license plate segmentation

Rong Zhu; Junfei Zhuo

doi:10.1117/12.832443

30 October 2009 An adaptive quantum-behaved particle swarm optimization approach for license plate segmentation

Rong Zhu, Junfei Zhuo

Proceedings Volume 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis; 74953Z (2009) https://doi.org/10.1117/12.832443
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

Automatic license plate segmentation plays an essential role in intelligent transportation systems. But it can be a challenging task when segmenting the vehicle images with poor quality in real-world applications. For segmenting license plates out of the vehicle images efficiently, a novel two-stage segmentation strategy that contains a rough localization stage and a fine segmentation stage is proposed in this paper. Firstly, during the rough localization stage, the texture characteristic of Chinese license plates is utilized to get candidate rectangle regions. The license plate region is then identified and extracted from these regions based on projection property and geometric information. In the fine segmentation stage, two enhancement algorithms are applied to improve image quality and reduce segmentation error. And then an adaptive threshold-based segmentation approach based on quantum-behaved particle swarm optimization is presented to deal with the threshold selection of distinguishing the constitution codes from the background in the obtained license plate region. The experiments of segmenting the vehicle images are illustrated to show that the proposed method can achieve an ideal segmentation result with less computational cost.

Citation Download Citation

Rong Zhu and Junfei Zhuo "An adaptive quantum-behaved particle swarm optimization approach for license plate segmentation", Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 74953Z (30 October 2009); https://doi.org/10.1117/12.832443

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