基于Hough变换的瞳孔识别方法研究与实现

张宏薇; 王仕洋; 李宪龙; 马海涛; 崔继承; 朱继伟; 李鸣皋; 高原; 韩磊; 周必业

doi:10.3788/YJYXS20163106.0621

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基于Hough变换的瞳孔识别方法研究与实现

显示与图像 | 更新时间：2020-08-12

- 基于Hough变换的瞳孔识别方法研究与实现
- Research and implementation of pupil recognition based on Hough transform
- 液晶与显示 2016年31卷第6期页码：621-625
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
  2. 吉林大学通信工程学院, 吉林长春 130012
  3. 海军总医院全军航海航空医学中心北京,100048
- 作者简介：
- 基金信息：
  
  国家重大科学仪器设备开发专项项目(No.2014YQ120351)()
- DOI：10.3788/YJYXS20163106.0621
  中图分类号： TP391
- 收稿日期：2015-12-25，
  
  修回日期：2016-01-24，
  
  纸质出版日期：2016-06-05
- 稿件说明：
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张宏薇, 王仕洋, 李宪龙等. 基于Hough变换的瞳孔识别方法研究与实现[J]. 液晶与显示, 2016,31(6): 621-625

ZHANG Hong-wei, WANG Shi-yang, LI Xian-long etc. Research and implementation of pupil recognition based on Hough transform[J]. , 2016,31(6): 621-625
张宏薇, 王仕洋, 李宪龙等. 基于Hough变换的瞳孔识别方法研究与实现[J]. 液晶与显示, 2016,31(6): 621-625 DOI： 10.3788/YJYXS20163106.0621.

ZHANG Hong-wei, WANG Shi-yang, LI Xian-long etc. Research and implementation of pupil recognition based on Hough transform[J]. , 2016,31(6): 621-625 DOI： 10.3788/YJYXS20163106.0621.

摘要

研究了一种改进的基于Hough变换圆检测的瞳孔识别方法。直接利用Hough变换圆检测进行瞳孔识别准确度低、运算量大

为此

本文提出了改进的识别方法。在实现Canny边缘检测的基础上

利用Hough变换进行求解时

限定检测的半径范围

在此范围内

根据参数空间圆的方程

求出对应的圆心坐标

得到最佳拟合圆

最终实现瞳孔识别以及中心点定位。在vc++6.0开发平台下

对150幅图像进行瞳孔识别检测实验

结果显示

直接利用Hough变换检测方法的准确率为70.7%

平均速度为1 s;本方法的准确率为94%

平均速度为0.45 s

可见相较于直接利用Hough变换进行瞳孔识别

本文方法在识别准确率和识别速度方面均有显著提高。结果表明

此方法能够快速而准确地进行瞳孔识别以及中心点定位。

Abstract

As traditional way for pupil recognition based on Hough transform circle detection has lower accuracy and speed

an improved method is proposed in this paper. After edge detection by Canny operator

Hough transform is performed in a limited radius. Within this range

finding the best fitting circle based on the circle equation in parameter space to get the center coordinates and radius

pupil recognition and center positioning are achieved ultimately. 150 images are used to test the effects of pupil recognition under vc++6.0 development platform. The experiments results show that the accuracy is 70.7% with an average speed of 1 s based on Hough transform which is used directly. In comparison the accuracy is 94% and the average speed is 0.45 s with the method proposed in this paper. It indicates that this method has a significant improvement on accuracy and speed. It can recognize the pupil and locate the center faster and has more accuracy.

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Keywords

references

王先梅,杨萍,王志良.多姿态眼球中的瞳孔定位算法[J].计算机辅助设计与图形学学报,2011,23(8):1427-1432. WANG X M, YANG P, WANG Z L. Pupil localization for multi-view eyeballs[J]. Journal of Computer-Aided Design & Computer Graphics, 2011, 23(8):1427-1432. (in Chinese)

赵青云.基于视频图像的瞳孔精确定位方法研究[D].长春:吉林大学,2012. ZHAO Q Y.Research on method of precise localization of the pupil based on video images[D]. Changchun:Jilin University, 2012. (in Chinese)

王长远,张景,李京京.瞳孔中心快速定位方法研究[J].计算机工程与应用,2011,47(24):196-198,201. WANG C Y, ZHANG J, LI J J. Research on algorithm for pupil-center rapid localization[J]. Computer Engineering and Applications, 2011, 47(24):196-198, 201. (in Chinese)

ZHU Y X, CARRAGHER B, MOUCHE F,et al. Automatic particle detection through efficient Hough transforms[J]. IEEE Transactions on Medical Imaging, 2003, 22(9):1053-1062.

WILDES R P. Iris recognition:an emerging biometric technology[J].Proceedings of the IEEE, 1997, 85(9):1348-1363.

宓翔,邱飞岳.基于CCD的瞳孔自动识别方法的设计与实现[J].轻工机械,2012,30(1):88-91,94. MI X, QIU F Y. Design and implementation of pupil automatic recognition based on CCD[J]. Light Industry Machinery, 2012, 30(1):88-91, 94. (in Chinese)

GONZALEZ R C,WOODS R E,EDDINS S L.数字图像处理[M].阮秋琦,阮宇智,译.第2版.北京:电子工业出版社,2003:463-474. GONZALEZ R C, WOODS R E, Eddins S L. Digital Image Processing [M]. RUAN Q Q, RUAN Y Z, Trans. 2nd ed. Beijing:Publishing House of Electronics Industry, 2003:463-474. (in Chinese)

CANNY J. A computational approach to edge detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, PAMI-8(6):679-698.

王林波,王延杰,邸男,等.基于几何特征的圆形标志点亚像素中心定位[J].液晶与显示,2014,29(6):1003-1009. WANG L B, WANG Y J, DI N,et al. Subpixel location of circle target center based on geometric features[J]. Chinese Journal of Liquid Crystals and Displays, 2014, 29(6):1003-1009. (in Chinese)

吕哲,王福利,常玉清.一种改进的Canny边缘检测算法[J].东北大学学报(自然科学版),2007,28(12):1681-1684. LV Z, WANG F L, CHANG Y Q. An improved canny algorithm for edge detection[J]. Journal of Northeastern University (Natural Science), 2007, 28(12):1681-1684. (in Chinese)

布拉德斯基,克勒.学习OpenCV[M].于仕琪,刘瑞祯,译.北京:清华大学出版社,2009:173,175-183. BRADSKI G, KAEHLER A.Learing OpenCV[M]. YU S Q, LIU R Z, Trans. Beijing:Tsinghua University, 2009:173, 175-183. (in Chinese)

朱桂英,张瑞林.基于Hough变换的圆检测方法[J].计算机工程与设计,2008,29(6):1462-1464. ZHU G Y, ZHANG R L. Circle detection using Hough transform[J]. Computer Engineering and Design, 2008, 29(6):1462-1464. (in Chinese)

杨全银.基于Hough变换的图像形状特征检测[D].济南:山东大学,2009. YANG Q Y.Shape features detection based on hough transform in images[D]. Jinan:Shandong University, 2009. (in Chinese)

张铮,王艳平,薛桂香.数字图像处理与机器视觉-Visual C++与Matlab实现[M].北京:人民邮电出版社,2010:338-340,350-353. ZHANG Z, WANG Y P, XUE G X. Digital Image Processing and Machine Vision-Visual C++ and Matlab Realization [M]. Beijing:Post & Telecom Press, 2010:338-340, 350-353. (in Chinese)

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