水珠边缘检测算法的研究及其在电力行业中的应用
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摘要
边缘是图像的最基本特征。边缘检测在图像识别、图像分割、图像增强以及图像压缩等领域中有着广泛的应用，也是它们的基础，一直是数字图像处理领域研究的热点和焦点。
水珠图像的背景十分复杂，加上水的透明性导致的目标与背景的灰度差较小和水对光的反射导致的对光一侧的边界极为模糊等原因，致使对水珠边界的识别非常困难。要达到好的边缘检测效果，本文首先对水珠图像的特征进行了分析，对图像的滤波处理算法进行了分析，采用高斯滤波和自适应滤波对水珠图像有较好的处理效果。
目前在边缘检测领域已经提出了许多方法，不同的边缘检测算法的性能具有很大的差异性，目前为止尚没有一种方法可以适合于所有图像。尤其，实际处理的图像一般都混有噪声，如何消除噪声干扰带来的伪边缘，并且同时保证边缘定位的准确性成为边缘检测需要解决的一个重要问题。因此，需要根据具体的应用设计新的边缘检测方法，或者对现有的方法进行改进以得到满意的边缘检测结果。针对水珠图像以及绝缘材料憎水性检测系统的需要，本文设计出了适合要求的算法和方案，并得到了较好的检测效果。
本文首先研究了经典的图像边缘检测算法和基于熵的自动阈值区域分割的水珠边缘检测方法，通过理论分析和计算，比较了各种算法对水珠图像边缘检测的优缺点，然后研究了基于Canny的边缘检测算法，针对传统Canny边缘检测算法的缺点对传统Canny边缘检测算法进行了改进。Canny自适应边缘检测算法在保持了传统Canny算子原有的定位准确，单边缘响应和信噪比高的优点的基础上，提高了Canny算子在提取水珠图像边缘细节信息和抑制假边缘方面的性能，在实际应用中对水珠图像起到了较好的检测效果。
本文用Visual C++6.0设计实现了绝缘子憎水性检测系统软件，在实验室阶段取得了较好的应用效果。本文最后还对边缘检测算法研究中遇到的相关问题进行了分析说明，如阈值选择，边缘模糊清晰化，水珠特征提取等。
Edge is one of the basic characters of an image. Edge detection has been widely used in image processing, image analysis and computer vision, and it is the basis of them. So, Edge detection is always the hot and focus in the research field of digital image processing.
The background of the bead image is complex. The transparence of the bead leads to the small difference of the gray level between the background and the object, the reflection of the light from water leads to the illegibility of the edge at the side facing the light, which leads to the difficulty of the recognition of the bead edge. In order to get a good edge detecting, firstly, the character and the filtering algorithm of the bead image are analyzed. The result shows that using Gaussian filter and adaptive filter can get satisfied result. There have been many algorithms proposed for the edge detection of images. But the different algorithm has different performance. It is hard to propose a general method of edge detection applied to all cases so far. Especially, images obtained from the real-world scenes are general buried in noise. Both edges and noise may be obtained in an attempt to detect edges from an image with a large amount of noise. How to detect edges reliably and accurately in the presence of noise has remained an important issue in the field of edge detection. So, it has been the focus of current research work to find new methods for edge detection with specific application requirements or to make improvements to existing method. This thesis sets about research for need of the water resist identify system and designs suited algorithms and project to resolve practical problems, and get satisfied result.
Firstly, the classic methods of image edge detection and automatic threshold region segmentation method based on entropy are introduced, this thesis compare the characteristic of all kinds of methods on bead image edge detection by using theoretical analysis and computation. Secondly, the Canny edge detection algorithm is analyzed. The algorithm of edge detection based on Canny theory is improved in allusion to the drawbacks of the traditional Canny edge detection algorithm. Adaptive Canny edge detection not only keeps the traditional Canny's excellent performance in good localization, only one response to single edge and good detection, but also improves the performance in detail of edge detection and in restraining false edge and it gets a good performance in practical edge detection.
Water resist identify system is realized using Visual C++6.0 program tool and it gets a good applied effect in the lab stage. Lastly, some correlative problems in the research of edge detection algorithm are analyzed and illuminated, such as threshold selection, illegible edge legible, bead characteristic pick-up etc.
水珠图像的背景十分复杂，加上水的透明性导致的目标与背景的灰度差较小和水对光的反射导致的对光一侧的边界极为模糊等原因，致使对水珠边界的识别非常困难。要达到好的边缘检测效果，本文首先对水珠图像的特征进行了分析，对图像的滤波处理算法进行了分析，采用高斯滤波和自适应滤波对水珠图像有较好的处理效果。
目前在边缘检测领域已经提出了许多方法，不同的边缘检测算法的性能具有很大的差异性，目前为止尚没有一种方法可以适合于所有图像。尤其，实际处理的图像一般都混有噪声，如何消除噪声干扰带来的伪边缘，并且同时保证边缘定位的准确性成为边缘检测需要解决的一个重要问题。因此，需要根据具体的应用设计新的边缘检测方法，或者对现有的方法进行改进以得到满意的边缘检测结果。针对水珠图像以及绝缘材料憎水性检测系统的需要，本文设计出了适合要求的算法和方案，并得到了较好的检测效果。
本文首先研究了经典的图像边缘检测算法和基于熵的自动阈值区域分割的水珠边缘检测方法，通过理论分析和计算，比较了各种算法对水珠图像边缘检测的优缺点，然后研究了基于Canny的边缘检测算法，针对传统Canny边缘检测算法的缺点对传统Canny边缘检测算法进行了改进。Canny自适应边缘检测算法在保持了传统Canny算子原有的定位准确，单边缘响应和信噪比高的优点的基础上，提高了Canny算子在提取水珠图像边缘细节信息和抑制假边缘方面的性能，在实际应用中对水珠图像起到了较好的检测效果。
本文用Visual C++6.0设计实现了绝缘子憎水性检测系统软件，在实验室阶段取得了较好的应用效果。本文最后还对边缘检测算法研究中遇到的相关问题进行了分析说明，如阈值选择，边缘模糊清晰化，水珠特征提取等。
Edge is one of the basic characters of an image. Edge detection has been widely used in image processing, image analysis and computer vision, and it is the basis of them. So, Edge detection is always the hot and focus in the research field of digital image processing.
The background of the bead image is complex. The transparence of the bead leads to the small difference of the gray level between the background and the object, the reflection of the light from water leads to the illegibility of the edge at the side facing the light, which leads to the difficulty of the recognition of the bead edge. In order to get a good edge detecting, firstly, the character and the filtering algorithm of the bead image are analyzed. The result shows that using Gaussian filter and adaptive filter can get satisfied result. There have been many algorithms proposed for the edge detection of images. But the different algorithm has different performance. It is hard to propose a general method of edge detection applied to all cases so far. Especially, images obtained from the real-world scenes are general buried in noise. Both edges and noise may be obtained in an attempt to detect edges from an image with a large amount of noise. How to detect edges reliably and accurately in the presence of noise has remained an important issue in the field of edge detection. So, it has been the focus of current research work to find new methods for edge detection with specific application requirements or to make improvements to existing method. This thesis sets about research for need of the water resist identify system and designs suited algorithms and project to resolve practical problems, and get satisfied result.
Firstly, the classic methods of image edge detection and automatic threshold region segmentation method based on entropy are introduced, this thesis compare the characteristic of all kinds of methods on bead image edge detection by using theoretical analysis and computation. Secondly, the Canny edge detection algorithm is analyzed. The algorithm of edge detection based on Canny theory is improved in allusion to the drawbacks of the traditional Canny edge detection algorithm. Adaptive Canny edge detection not only keeps the traditional Canny's excellent performance in good localization, only one response to single edge and good detection, but also improves the performance in detail of edge detection and in restraining false edge and it gets a good performance in practical edge detection.
Water resist identify system is realized using Visual C++6.0 program tool and it gets a good applied effect in the lab stage. Lastly, some correlative problems in the research of edge detection algorithm are analyzed and illuminated, such as threshold selection, illegible edge legible, bead characteristic pick-up etc.
引文
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[10] 徐光宝，姜东焕．自适应多尺度Canny边缘检测[J]．山东建筑大学学报，2006，21(4)：360～363
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[3] Canny J.A.Computational Approach to Edge Detection[J]. IEEE Transactions on Patten Analysis and Machine Intelligence,1986,8(6):679～698
[4] 万力，易昂，傅明．一种基于Canny算法的边缘提取改善方法[J]．计算技术与自动化，2003，22(1)：24～26
[5] 杨枝灵，王开等．Visual c++数字图像获取处理及实践应用[M]．人民邮电出版社，2003
[6] Demigny D, Kamle T. A Discrete Expression of Canny' Criteria for Step Detector Performances Evaluation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997,19(6): 1199～1211
[7] Worthington P L. Enhanced Canny Edge Detection Using Curvature Consistency[C]. Proceedings International Conference on Pattern Recognition, IEEE, Computer SOC Press,2002:596～599
[8] 王植，贺赛先．一种基于Canny理论的自适应边缘检测方法[J]．中国图象图形学报，2004，9(8)：957～962
[9] 王娜，李霞．一种新的改进Canny边缘检测算法[J]．深圳大学学报理工版，2005，22(2)：149～152
[10] 徐光宝，姜东焕．自适应多尺度Canny边缘检测[J]．山东建筑大学学报，2006，21(4)：360～363
[11] 杨述斌，彭复员，张增常．多尺度自适应加权形态边缘检测方法[J]．华中科技大学学报，2002，30(10)：87～89
[12] Louisa Lam,Seong-Whan Lee. Thinning Methodologies-A Comprehensive Survey[J].IEEE Transactions of Pattern Analysis and Machine Intelligence,1992,14(9):869～885
[13] 林晓梅，李琳娜，薄万宝．空间域图像去噪方法[J]．长春工业大学学报，2004，25(1)：29～31
[14] 曹婧华，冉彦忠，刘沛先．Canny算法在细胞图像边缘检测中的应用[J]．湖南工程学院学报，2006，16(3)：69～72
[15] 魏伟波，芮筱亭．图像边缘检测方法研究[J]．计算机工程与应用，2006，27(6)：11～15
[16] 李秀峰，苏兰海．基于中值滤波和数学形态学的图像边缘检测[J]．计算机与信息技术，2006．2：15～18
[17] 曾欢，王浩．图像边缘检测算法的性能比较与分析[J]．现代电子技术，2006，29(1)：21～25
[18] Vinciane Lacroix. The Primary Raster: a Multiresolution Image Description. IEEE Transactions on Information Theory,1990,903～907
[19] 沈洁，杜宇人，高浩军．图像边缘检测技术研究[J]．信息技术，2005，12(9)：69～85
[20] 袁涛，张爱琳．图像的边缘检测算法实现[J]．系统工程与电子技术，2006，27(3)：19～24
[21] 魏光杏，吴锡生．新型边缘检测法[J]．计算机工程与设计，2007，28(4)：25～30
[22] 王强．图像分割中阈值的选取研究及算法实现[J]．计算机与现代化，2006，14(10)：42～46
[23] J.Morlet, G.arens, E.Foureau, and D.Giard. Wave Propagation and Sampling Theory. Part 2: Sampling Theory and Complex Waves. Geophysics. 1982,47(1):222～236
[24] 罗文村，郭伟斌．图像阈值分割方法的比较与分析[J]．现代计算机．总第103期．2000．11
[25] 文山．多尺度边缘检测算子的构造[J]．电子学报，2005，33(6)：69～74
[26] 陈桂友，孙同景，雷印胜．一种基于自适应滤波的指纹图像增强算法[J]．电子测量与仪器学报，2006，20(6)：112～117
[27] 韩军伟．景物边缘提取技术的研究与应用．[硕士学位论文]．陕西：西北工业大学，2001．2
[28] Donoho David L. Denoising by Softthresholding[J].IEEE Transactions on Information Theory, 1995,41 (3):613～627
[29] 舒海燕．图像目标识别技术的研究与应用．[硕士学位论文]．陕西：西北工业大学．2002．3
[30] 何斌，马天予．Visual C++数字图像处理[M]，北京：电子工业出版社，2001
[31] 阮秋琦．数字图像处理学[M]．北京：电子工业出版社，2001
[32] 贾永红．数字图像处理[M]．武汉：武汉大学出版社，2003
[33] D. Marr, E. Hundredth. Theory of Edge Detection[A].Proceedings of the Royal Society of London[C].London, England, 1980, B207:187～217
[34] B.Buxton. Early Image Processing Structural Techniques Motivated by Human Visual Response [D].Guildford, England: University of Surrey, 1984
[35] 朱虹，廖学成，乐静．数字图像处理[M]．科学出版社，2002
[36] 刘丹．计算机图像处理的数学和算法基础[M]．国防工业出版社，2005
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