Method of modification and feature selection of Zernike moment

Computer Engineering and Applications ›› 2012, Vol. 48 ›› Issue (20): 182-186.

Previous Articles Next Articles

Method of modification and feature selection of Zernike moment

CHEN Ling1，3, LI Xiying2，3, LUO Ling2，3

1.School of Physics and Engineering, Sun Yat-Sen University, Guangzhou 510275, China
2.School of Engineering，Sun Yat-Sen University，Guangzhou 510275, China
3.Guangdong Provincial Key Laboratory of Intelligent Transportation System, Guangzhou 510275, China

Online:2012-07-11 Published:2012-07-10

一种改进和提取Zernike特征矩的方法

陈玲1，3，李熙莹2，3，罗玲2，3

1.中山大学物理科学与技术学院，广州 510275
2.中山大学工学院，广州 510275
3.广东省智能交通系统重点实验室，广州 510275

Abstract

Abstract: The accuracy of Zernike moment is a significant problem in pattern recognition due to the discrimination of image. By transforming the square image to an abstract circle image using a square-to-circle method, the computation of Zernike moment is speeded up, and the representation ability of Zernike moment is improved. Combining with statistical feature selection theory, suitable eigenvector construction method is proposed. The result of plate license character recognition shows this method can work efficiently.

Key words: Zernike moment, square to circle, feature selection

摘要： 数字图像的离散性使Zernike矩的计算存在误差，降低了特征描述能力。采用一种方圆转换方法将矩形图像转化为抽象的圆形图像，计算转化后图像Zernike矩，改进Zernike矩的计算方法，从而减少计算误差，提高计算速率，提高Zernike矩的特征描述能力。同时根据统计理论，提出一种规范的特征提取方法。经车牌字符识别实验验证，改进方法能够很好地提取图像特征，用于模式识别。

关键词: Zernike矩, 方圆变换, 特征选择

CHEN Ling1，3, LI Xiying2，3, LUO Ling2，3. Method of modification and feature selection of Zernike moment[J]. Computer Engineering and Applications, 2012, 48(20): 182-186.

陈玲1，3，李熙莹2，3，罗玲2，3. 一种改进和提取Zernike特征矩的方法[J]. 计算机工程与应用, 2012, 48(20): 182-186.

[1]	LI Li, JI Xinyuan, SONG Song. Prediction Model for Number of Software Defects in Loop [J]. Computer Engineering and Applications, 2021, 57(7): 158-163.
[2]	LI Jingxing, YANG Youlong. Feature Selection of Markov Blanket for High Dimensional Data [J]. Computer Engineering and Applications, 2021, 57(6): 58-66.
[3]	LIN Weixing, WANG Yujia, CHEN Wanfen, LIANG Haina. High-Dimensional Data Feature Selection Algorithm Based on Multifactor Particle Swarm Optimization [J]. Computer Engineering and Applications, 2021, 57(22): 199-207.
[4]	LI Longzhu, LIN Yaojin, LYU Yan, LU Shun, WANG Chenxi. Online Streaming Feature Selection Algorithm Using Neighborhood Information Interaction [J]. Computer Engineering and Applications, 2021, 57(21): 102-108.
[5]	CHEN Qianru, LI Yali, XU Kequan, LIU Yilong, WANG Shuqin. WKNN Feature Selection Method Based on Self-Tuning Adaptive Genetic Algorithm [J]. Computer Engineering and Applications, 2021, 57(20): 164-171.
[6]	WU Weijie, ZHANG Jingxiang. Random Forest Feature Selection Algorithm Based on Categorization Information and Application [J]. Computer Engineering and Applications, 2021, 57(17): 147-156.
[7]	QIU Yunfei, GAO Huacong. Hybrid Filter and Improved Adaptive GA for Feature Selection [J]. Computer Engineering and Applications, 2021, 57(11): 95-102.
[8]	HUO Lin, LU Yinli. Improved Particle Swarm Optimization for Android Malware Detection [J]. Computer Engineering and Applications, 2020, 56(7): 96-101.
[9]	LIAO Wenxiong, ZENG Bi, LIANG Tiankai, XU Yayun, ZHAO Junfeng. Personal Credit Risk Assessment Method for High-Dimensional Data [J]. Computer Engineering and Applications, 2020, 56(4): 219-224.
[10]	PENG Ming, ZHANG Haipeng. Unsupervised Feature Selection via Schatten-p Norm and Feature Self-Representation [J]. Computer Engineering and Applications, 2020, 56(23): 45-52.
[11]	LIU Feng, Godfred Kim Mensah, LI Xinyun, LIU Hongli, LI Yao, GUO Hao. Abnormal Topological Analysis and Classification Research of Uncertain Brain Networks [J]. Computer Engineering and Applications, 2020, 56(2): 127-132.
[12]	YUE Peng, HOU Lingyan, YANG Dali, TONG Qiang. XLC-Stacking Method for Disease Diagnosis Based on XGBoost Feature Selection [J]. Computer Engineering and Applications, 2020, 56(17): 136-141.
[13]	HUANG Xin, MO Haimiao, ZHAO Zhigang, ZENG Min. Research on Discrete Enhanced Fireworks Algorithm and [kNN] in Feature Selection [J]. Computer Engineering and Applications, 2020, 56(16): 112-117.
[14]	ZHOU Wanying, MA Yingcang, XU Qiuxia, ZHENG Yi. Unsupervised Feature Selection Algorithm Based on Maximum Entropy and [l2,0] Norm Constraints [J]. Computer Engineering and Applications, 2020, 56(11): 51-59.
[15]	CHEN Jia, LIU Dongxue, WU Dashuo. Stock Index Forecasting Method Based on Feature Selection and LSTM Model [J]. Computer Engineering and Applications, 2019, 55(6): 108-112.

Method of modification and feature selection of Zernike moment

一种改进和提取Zernike特征矩的方法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics