[1]吴加明,吴一全.基于Tent映射CPSO和车牌纹理特征的车牌定位[J].智能系统学报,2011,6(04):333-338.
 WU Jiaming,WU Yiquan.License plate location based on texture features and tent chaotic particle swarm optimization[J].CAAI Transactions on Intelligent Systems,2011,6(04):333-338.
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基于Tent映射CPSO和车牌纹理特征的车牌定位(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第6卷
期数:
2011年04期
页码:
333-338
栏目:
学术论文—智能系统
出版日期:
2011-08-25

文章信息/Info

Title:
License plate location based on texture features and tent chaotic particle swarm optimization
文章编号:
1673-4785(2011)04-0333-06
作者:
吴加明吴一全
南京航空航天大学 信息科学与技术学院,江苏 南京210016
Author(s):
WU Jiaming WU Yiquan
School of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
关键词:
车牌纹理特征Tent映射混沌粒子群优化车牌定位
Keywords:
texture feature tent map chaotic particle swarm optimization license plate location
分类号:
TP18;TN911.73
文献标志码:
A
摘要:
针对现有车牌定位算法定位准确率不高和速度慢等问题,结合车牌纹理特征,提出了一种基于Tent映射混沌粒子群(CPSO)的车牌精确定位算法.首先用基于二维直方图区域斜分的OTSU方法对车牌图像做二值化处理;接着使用三组一维滤波器获取其二值纹理特征向量.然后利用基于Tent映射CPSO快速准确的全局搜索能力,结合二值纹理特征向量构造适应度函数,并引入车牌纹理的一致性度量作为判决条件,找到车牌区域的最佳定位参量.最后,与基于遗传算法(GA)和基本粒子群算法(BPSO)的定位方法进行了比较.实验结果表明,该方法适应性强,定位效果较好,运行时间更短.
Abstract:
Considering the problems of the low precision ratio and slow arithmetic speed of license plate location, an accurate license plate location method based on tent chaotic particle swarm optimization (TCPSO) was proposed by combining the texture features. First, binarization was adopted to segment the license plate image by the OTSU method, which is based on a 2D histogram oblique. Then the texture feature vector was obtained by three onedimensional filters. With the rapid and accurate searching ability, the best location parameters of license plate area were found by constructing the fitness function with the texture feature vector when introducing the texture coherence into the judgment. At last, the proposed method was compared with a genetic algorithm (GA) and BPSO. The experimental results show that the proposed method has stronger adaptability, better location effect, and shorter running time.

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备注/Memo

备注/Memo:
收稿日期: 2010-10-03.
基金项目:国家自然科学基金资助项目(60872065).
通信作者:吴加明. E-mail: wujiaming42@yahoo.com.cn.
作者简介:
吴加明, 男, 1986年生,硕士研究生, 主要研究方向为图像处理、车牌识别、信号处理等.
吴一全, 男, 1963年生, 教授, 博士, 主要研究方向为图像处理与模式识别、目标检测与跟踪、智能信息处理等. 发表学术论文90余篇.
更新日期/Last Update: 2011-09-30