数字图像边缘检测技术的研究
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摘要
随着计算机技术的飞速发展,图像边缘检测已成为图像处理的重要内容,它是图像分析的基本问题,是图像分割、特征提取和图像识别的前提。本文的主要内容如下。
首先,介绍了数字图像处理的概念及其应用领域、边缘检测研究的背景意义,历史现状,以及边缘检测的一些基本概念。
然后,分别介绍了经典的图像边缘检测算子,如Robert算子、Sobel算子、Prewitt算子等,并通过理论分析和仿真计算比较了他们各自的优缺点及适用性。接着概述了几种新的边缘检测方法,如小波理论、数学形态学、模糊理论等。在本文的第四章里,讨论了基于线性滤波技术的边缘检测算法:Marr-Hildreth方法和Canny算法。
最后,提出了一种基于各向异性扩散方程的Canny边缘检测算法。Canny边缘检测算法由于使用高斯滤波对图像进行平滑,往往使得算法的信噪比和定位精度下降,从而产生一些虚假边缘,使角点变圆。针对Canny算法所出现的问题,运用各向异性扩散方程代替高斯滤波,并对扩散后的图像做图像增强。实验结果表明,改进后的算法有效地提高了边缘检测的准确性,得到了比较理想的边缘检测效果。
The image edge detection has become one of the most important parts of image processing with the development of computer technology. Image edge detection is the first step of image analysis, also the basis of image segmentation, feature extraction and image recognition. The main content of this dissertation is described as follows.
Firstly, digital image processing and its applications are introduced. Then, the background, the significance and also the development status of the image edge detection technique are introduced, next to this, some basic knowledge of the image edge detection are discussed.
Secondly, some classical edge detection algorithms such as Roberts, Sobel, Prewitt are discussed. Theory analysis and experiments are carried out to compare their advantages and disadvantages. Some new technology about edge detection, such as wavelet, math morphology, rough set theory, are introduced. In the fourth chapter, two kinds of edge detection algorithms based on linear filtering, i.e., Marr-Hildreth algorithm and Canny algorithm, are discussed.
Finally, an improved Canny edge detection algorithm based on anisotropic diffusion equation is proposed. The canny edge detection algorithm uses Gaussian filter for smoothing the image, which may lead to lower SNR and higher edge location error, and therefore may produce false edge and corner roundness. To solve these problems, an improved edge detection algorithm is proposed by replacing Gaussian filter with anisotropic diffusion equations, and the image enhancement is carried out after diffusion. Experiment results show that the accuracy of edge detection is improved evidently, and a much better edge detection effect is obtained.
首先,介绍了数字图像处理的概念及其应用领域、边缘检测研究的背景意义,历史现状,以及边缘检测的一些基本概念。
然后,分别介绍了经典的图像边缘检测算子,如Robert算子、Sobel算子、Prewitt算子等,并通过理论分析和仿真计算比较了他们各自的优缺点及适用性。接着概述了几种新的边缘检测方法,如小波理论、数学形态学、模糊理论等。在本文的第四章里,讨论了基于线性滤波技术的边缘检测算法:Marr-Hildreth方法和Canny算法。
最后,提出了一种基于各向异性扩散方程的Canny边缘检测算法。Canny边缘检测算法由于使用高斯滤波对图像进行平滑,往往使得算法的信噪比和定位精度下降,从而产生一些虚假边缘,使角点变圆。针对Canny算法所出现的问题,运用各向异性扩散方程代替高斯滤波,并对扩散后的图像做图像增强。实验结果表明,改进后的算法有效地提高了边缘检测的准确性,得到了比较理想的边缘检测效果。
The image edge detection has become one of the most important parts of image processing with the development of computer technology. Image edge detection is the first step of image analysis, also the basis of image segmentation, feature extraction and image recognition. The main content of this dissertation is described as follows.
Firstly, digital image processing and its applications are introduced. Then, the background, the significance and also the development status of the image edge detection technique are introduced, next to this, some basic knowledge of the image edge detection are discussed.
Secondly, some classical edge detection algorithms such as Roberts, Sobel, Prewitt are discussed. Theory analysis and experiments are carried out to compare their advantages and disadvantages. Some new technology about edge detection, such as wavelet, math morphology, rough set theory, are introduced. In the fourth chapter, two kinds of edge detection algorithms based on linear filtering, i.e., Marr-Hildreth algorithm and Canny algorithm, are discussed.
Finally, an improved Canny edge detection algorithm based on anisotropic diffusion equation is proposed. The canny edge detection algorithm uses Gaussian filter for smoothing the image, which may lead to lower SNR and higher edge location error, and therefore may produce false edge and corner roundness. To solve these problems, an improved edge detection algorithm is proposed by replacing Gaussian filter with anisotropic diffusion equations, and the image enhancement is carried out after diffusion. Experiment results show that the accuracy of edge detection is improved evidently, and a much better edge detection effect is obtained.
引文
[1]王郑耀.数字图像的边缘检测.西安:西安交通大学出版社,2003.
[2]T.Poggio,H.Voorhees,A.Yuille.A regularized solution to edge detection.Journal of Complexity,1988,(4):106-123.
[3]V.S.Nalwa,T.O.Binford.On detecting edges.IEEE Trans.Pattern Anal.Mach.Intell.,1986,(8):699-714.
[4]S.Sarkar,K.L.Boyer.Optimal infinite impulse response zero crossing based edge detectors.Image Understanding,1991,(54):224-243.
[5]H.Moon,R.Chellappa,A.Rosenfield.Optimal edge based shape detection.IEEE Trans.On Image Processing,2002,11(11):1209-1127.
[6]J.S.Huang,D.H.Tseng.Statistical theory of edge detection.Computer Vision,Graphics,And Image Processing,1988,(43):337-346.
[7]S.Konishi,A.Yuille,J.Coughlan.A statistical approach to multi scale edge detection.Image and Vision computing,2003,(21):37-48.
[8]S.C.Douglas,T.H.Y.Meng.Design of edge detection templates using a neural network.Proc.International Joint Conference on Neural Networks.1990,2:331-334.
[9]D.S.Kim,W.H.Lee,I.S.Kweon.Automatic edge detection using 3~*3 ideal binary pixel patterns and fuzzy based edge thresholding.Pattern Recognition Letters,2004,(25):101-106.
[10]Z.Lei,B.Paul.Edge detection by scale multiplication in wavelet domain.Pattern Recognition Letters,2002,23(14):1771-1784.
[11]Q.Tian,X.Li,N.M.Bilgutay.Multiple target detection using split spectrum processing and group delay moving entropy.IEEE Trans on UFFC.1995,42(6):1075-1086.
[12]M.Brejl,M.Sonka.Directional 3D edge detection in anisotropic data:detector design and performance assessment.Computer Vision and Image Understanding,2000,(77):84-110.
[13]J.M.Geusebroek,A.Dev,R.Boomgarrd,A.W.M.Smeulders,F.Cornelissen,H.Geerts.Color invariant edge detection.Scale Space Theories in Computer Vision,Springer Verlag Gmbh,Berlin.1999,1682:459-464
[14]D.Borghys,V.Lacroix,C.Perneel.Edge and line detection in polarimetric SAR images.Proc.Int.Conf.on Pattern Recognition,Quebec,Canada.2002,2:921-924.
[15]李炜,黄心汉.车辆自动识别系统中车牌分割的研究.信号处理,2000,16:41-44.
[16]T.Kawaguchi,M.Rizon.Iris detection using intensity and edge information.Pattern Recognition,2003,(36):549-562.
[17]邵凌,张立明.一种基于肤色和模板的人脸检测方法.红外与毫米波学报,2000,19(3):209-214.
[18]章毓晋.图像处理和分析(图像工程上册),北京:清华大学出版社.1999:181-182.
[19]贾云得.机器视觉,北京:科学出版社.2000:84-86.
[20]张小琳.图像边缘检测技术综述.高能量密度物理,2007(1):37-40.
[21]雷丽珍.数字图像边缘检测方法的探讨.测绘通报,2006,3:40-42.
[22]L.G.Roberts,Machine perception of three-dimension solids,Optimal and Electro-Optimal Information Processing,MA:MIT Press,1965,99-197.
[23]L.Sobel,Camera models and machine perception,PhD Theses,Standford University,Standford,CA,1970.
[24]J.Prewitt,Object enhancement and extraction,Picture Process.Psychopict,1970,75-149.
[25]R.Kirsch.Computer determination of the constituent structure of biological images,Computer Biomedical Research,1971:315-328
[26]郑南宁.计算机视觉与模式识别,北京:国防工业出版社,1998.
[27]季虎,孙即详,邵晓芳,等.图像边缘提取方法及展望.计算机工程与应用,2004,40(14):70-73.
[28]程正兴.小波分析在图像处理中的应用.工程数学学报,2001,(12):57-86.
[29]王建中,赵军.图像边缘提取的小波多孔算法及改进.武汉理工大学学报,2004,26(1):76-79.
[30]赵登峰,许纯新.小波分析及其在数字图像处理中的应用.同济大学学报,2001,29(9):1024-1057.
[31]崔屹.图像处理与分析—数学形态学方法及应用,北京:科学出版社,2000.
[32]冯俊萍,赵转萍.基于数学形态学的图像边缘检测技术.航空计算技术,2004,34(3):53-56.
[33]王树文,闫成新,张天序.数学形态学在图像处理中的应用.计算机工程与应用,2004,3:89-92.
[34]杨平先,孙兴波.一种改进多尺度形态学边缘检测算法.光电工程,2005,32(11):72-75.
[35]张世华,宋振明.一种基于模糊增强的图像边缘提取改进算法.湖南工程学院学报,2006,65-68.
[36]施成湘,杨丹,尚晋.扩展的多尺度模糊边缘检测.计算机工程与应用,2006,65-67.
[37]白建明,王之琼.分形理论在X光片图像边缘增强中的应用.黑龙江医药科学,2006,29(1):78-79.
[38]崔旭东,邱春蓉,刘瑞根.用标记松弛法检测闪光图像边缘.光电工程,2001,28(4):42-45.
[39]肖锋.基于BP神经网络的数字图像边缘检测算法的研究.西安科技大学学报,2005,25(3):372-37.
[40]D.C.Marr,E.Hildreth.Theory of edge detection.Proc.Roy.Soc.London,1980,275:187-217.
[41]J.Canny.A computational approach to edge detection.IEEE Trans.PAMI,1986,8(6):678-69.
[42]Julez.A method of coding TV signals based on edge detection.Bell System Tech Compression Video Television,1959,6(38):1001-1020.
[43]罗军辉,冯平,等.Matlab7.0在图像处理中的应用,北京:机械工业出版社,2005.
[44]Henri Maitre等,著;孙洪,译.现代数字图像处理,北京:电子工业出版社,2006:156.
[45]P.Perona,J.Malik.Scale-space and edge detection using anisotropic diffusion.IEEE Trans,1990:PAMI-12(7):629-639.
[46]梅跃松,杨树兴,莫波.基于Canny算子的改进的图像边缘检测方法.激光与红外,2006,6(36):501-503.
[47]范彦革,刘旭.各向异性扩散的研究.计算机工程与应用2006,29:55-59
[48]张小洪,杨丹,刘亚威.基于Canny算子的改进型边缘检测算法.计算机工程与应用,2003,29:113-115.
[49]张洁,檀结庆.基于各向异性扩散方程的Canny边缘检测算法.计算机应用,2008,28(8):2049-2051.
[2]T.Poggio,H.Voorhees,A.Yuille.A regularized solution to edge detection.Journal of Complexity,1988,(4):106-123.
[3]V.S.Nalwa,T.O.Binford.On detecting edges.IEEE Trans.Pattern Anal.Mach.Intell.,1986,(8):699-714.
[4]S.Sarkar,K.L.Boyer.Optimal infinite impulse response zero crossing based edge detectors.Image Understanding,1991,(54):224-243.
[5]H.Moon,R.Chellappa,A.Rosenfield.Optimal edge based shape detection.IEEE Trans.On Image Processing,2002,11(11):1209-1127.
[6]J.S.Huang,D.H.Tseng.Statistical theory of edge detection.Computer Vision,Graphics,And Image Processing,1988,(43):337-346.
[7]S.Konishi,A.Yuille,J.Coughlan.A statistical approach to multi scale edge detection.Image and Vision computing,2003,(21):37-48.
[8]S.C.Douglas,T.H.Y.Meng.Design of edge detection templates using a neural network.Proc.International Joint Conference on Neural Networks.1990,2:331-334.
[9]D.S.Kim,W.H.Lee,I.S.Kweon.Automatic edge detection using 3~*3 ideal binary pixel patterns and fuzzy based edge thresholding.Pattern Recognition Letters,2004,(25):101-106.
[10]Z.Lei,B.Paul.Edge detection by scale multiplication in wavelet domain.Pattern Recognition Letters,2002,23(14):1771-1784.
[11]Q.Tian,X.Li,N.M.Bilgutay.Multiple target detection using split spectrum processing and group delay moving entropy.IEEE Trans on UFFC.1995,42(6):1075-1086.
[12]M.Brejl,M.Sonka.Directional 3D edge detection in anisotropic data:detector design and performance assessment.Computer Vision and Image Understanding,2000,(77):84-110.
[13]J.M.Geusebroek,A.Dev,R.Boomgarrd,A.W.M.Smeulders,F.Cornelissen,H.Geerts.Color invariant edge detection.Scale Space Theories in Computer Vision,Springer Verlag Gmbh,Berlin.1999,1682:459-464
[14]D.Borghys,V.Lacroix,C.Perneel.Edge and line detection in polarimetric SAR images.Proc.Int.Conf.on Pattern Recognition,Quebec,Canada.2002,2:921-924.
[15]李炜,黄心汉.车辆自动识别系统中车牌分割的研究.信号处理,2000,16:41-44.
[16]T.Kawaguchi,M.Rizon.Iris detection using intensity and edge information.Pattern Recognition,2003,(36):549-562.
[17]邵凌,张立明.一种基于肤色和模板的人脸检测方法.红外与毫米波学报,2000,19(3):209-214.
[18]章毓晋.图像处理和分析(图像工程上册),北京:清华大学出版社.1999:181-182.
[19]贾云得.机器视觉,北京:科学出版社.2000:84-86.
[20]张小琳.图像边缘检测技术综述.高能量密度物理,2007(1):37-40.
[21]雷丽珍.数字图像边缘检测方法的探讨.测绘通报,2006,3:40-42.
[22]L.G.Roberts,Machine perception of three-dimension solids,Optimal and Electro-Optimal Information Processing,MA:MIT Press,1965,99-197.
[23]L.Sobel,Camera models and machine perception,PhD Theses,Standford University,Standford,CA,1970.
[24]J.Prewitt,Object enhancement and extraction,Picture Process.Psychopict,1970,75-149.
[25]R.Kirsch.Computer determination of the constituent structure of biological images,Computer Biomedical Research,1971:315-328
[26]郑南宁.计算机视觉与模式识别,北京:国防工业出版社,1998.
[27]季虎,孙即详,邵晓芳,等.图像边缘提取方法及展望.计算机工程与应用,2004,40(14):70-73.
[28]程正兴.小波分析在图像处理中的应用.工程数学学报,2001,(12):57-86.
[29]王建中,赵军.图像边缘提取的小波多孔算法及改进.武汉理工大学学报,2004,26(1):76-79.
[30]赵登峰,许纯新.小波分析及其在数字图像处理中的应用.同济大学学报,2001,29(9):1024-1057.
[31]崔屹.图像处理与分析—数学形态学方法及应用,北京:科学出版社,2000.
[32]冯俊萍,赵转萍.基于数学形态学的图像边缘检测技术.航空计算技术,2004,34(3):53-56.
[33]王树文,闫成新,张天序.数学形态学在图像处理中的应用.计算机工程与应用,2004,3:89-92.
[34]杨平先,孙兴波.一种改进多尺度形态学边缘检测算法.光电工程,2005,32(11):72-75.
[35]张世华,宋振明.一种基于模糊增强的图像边缘提取改进算法.湖南工程学院学报,2006,65-68.
[36]施成湘,杨丹,尚晋.扩展的多尺度模糊边缘检测.计算机工程与应用,2006,65-67.
[37]白建明,王之琼.分形理论在X光片图像边缘增强中的应用.黑龙江医药科学,2006,29(1):78-79.
[38]崔旭东,邱春蓉,刘瑞根.用标记松弛法检测闪光图像边缘.光电工程,2001,28(4):42-45.
[39]肖锋.基于BP神经网络的数字图像边缘检测算法的研究.西安科技大学学报,2005,25(3):372-37.
[40]D.C.Marr,E.Hildreth.Theory of edge detection.Proc.Roy.Soc.London,1980,275:187-217.
[41]J.Canny.A computational approach to edge detection.IEEE Trans.PAMI,1986,8(6):678-69.
[42]Julez.A method of coding TV signals based on edge detection.Bell System Tech Compression Video Television,1959,6(38):1001-1020.
[43]罗军辉,冯平,等.Matlab7.0在图像处理中的应用,北京:机械工业出版社,2005.
[44]Henri Maitre等,著;孙洪,译.现代数字图像处理,北京:电子工业出版社,2006:156.
[45]P.Perona,J.Malik.Scale-space and edge detection using anisotropic diffusion.IEEE Trans,1990:PAMI-12(7):629-639.
[46]梅跃松,杨树兴,莫波.基于Canny算子的改进的图像边缘检测方法.激光与红外,2006,6(36):501-503.
[47]范彦革,刘旭.各向异性扩散的研究.计算机工程与应用2006,29:55-59
[48]张小洪,杨丹,刘亚威.基于Canny算子的改进型边缘检测算法.计算机工程与应用,2003,29:113-115.
[49]张洁,檀结庆.基于各向异性扩散方程的Canny边缘检测算法.计算机应用,2008,28(8):2049-2051.