指针万用表表壳图像处理关键技术研究
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摘要
指针式万用表表壳图像的自动处理和识别,是基于机器视觉的指针式万用表的自动检定系统的一个重要组成部分。本文采用机器视觉、图像处理等技术实现了指针式万用表表壳图像的自动处理和识别,并重点研究了表壳图像的分割、倾斜表壳图像的处理和表壳字符的分割识别。
在图像的分割、倾斜处理校正及字符识别方面,国内外都取得了一些研究进展,产生了一些比较成熟的处理方法。但是,针对常用的指针万用表表壳图像的处理,在国内外的研究中比较少见,研究资料也相当匮乏。因此,基于机器视觉的指针万用表表壳图像的自动处理和识别,具有重大的实际意义和经济效益。
本文主要做了以下几个方面的工作:
1、研究了指针式万用表表壳图像的预处理和分割方法:
对指针万用表表壳图像进行了较好的预处理,分割提取了多量程切换开关的指针、黑色圆盘刻度线,并设计算法对刻度线进行精确修补。分割出了黑色圆盘周围的刻度值,对指针对应刻度值位置进行了识别,并分割提取出刻度线所指的字符区域。研究了针对指针万用表表壳图像的字符切分和粘连字符的处理方法。
2、研究了指针式万用表表壳图像的字符识别和类别识别方法:
采用模板匹配实现了字符识别,详细介绍了模板的制作以及字符模板的匹配计算方法。针对指针万用表表壳图像的特点,研究了字符的类别识别方法:区域颜色识别方法和弧度线包括刻度线的范围识别方法,并设计实现了字符识别算法和字符类别识别算法。
3、设计实现指针万用表表壳图像处理系统,实现了自动识别,具有一定的实用价值:
通过Matlab7.0软件实现了上述方法和算法,对指针万用表表壳图像进行了仿真分析和处理。设计了表壳图像处理系统的软件,通过实际表壳图像处理,取得了较好的效果。
The automatic processing and recognition of the shell image of pointer multi-meter is one of an important component for the automatic calibration devices which is based on machine vision. In this thesis, the technology of machine vision and image processing has been applied to process the automatic processing and recognition of the shell image of pointer multi-meter, which is stressed on image segmentation, tilt correction and character segmentation and recognition.
In the aspect of segmentation, tilt correction and character recognition, the technology has become mature and it has acquired great achievements all around the world. However, the research of the shell image of the pointer multi-meter is inadequate and the analytic documents about it are also seldom. As a result, it is of great significant to research on the shell image processing.
This thesis mainly did something and got some achievements as follows:
1. Research the pre-processing and segmentation of the shell image of the pointer multi-meter.
Research the pre-processing of the shell image of the multi-meter, segment and extract the pointer of the switch to multi-meter measuring range and the compass display of the black disk. Design algorithms to realize accurate image inpainting for the compass display. Segment and extract the graduated values round the black disk, then recognize the location of the pointer which relevant to the graduated value. At last, extract the text region. Research the character segmentation and character conglutination for the shell image of the point multi-meter.
2. Research the text recognition and classification recognition of the shell image of the pointer multi-meter.
Realize the text recognition through template matching. Introduce the methods of how to make the template and how to calculate the template matching. According to the feature of the shell image of the pointer multi-meter, research the character category recognition: the method of regional color recognition and the method of the range of curve contain the compass display. Then design and realize the algorithm of the text recognition and character category recognition.
3. Design and realize the image processing system of the shell image of pointer multi-meter.
Through using the software of Matlab7.0, good results have acquired from the experiment of the automatic processing and recognition of the shell image of pointer multi-meter. The algorithms and methods have been realized and applied to the automatic calibration system.
在图像的分割、倾斜处理校正及字符识别方面,国内外都取得了一些研究进展,产生了一些比较成熟的处理方法。但是,针对常用的指针万用表表壳图像的处理,在国内外的研究中比较少见,研究资料也相当匮乏。因此,基于机器视觉的指针万用表表壳图像的自动处理和识别,具有重大的实际意义和经济效益。
本文主要做了以下几个方面的工作:
1、研究了指针式万用表表壳图像的预处理和分割方法:
对指针万用表表壳图像进行了较好的预处理,分割提取了多量程切换开关的指针、黑色圆盘刻度线,并设计算法对刻度线进行精确修补。分割出了黑色圆盘周围的刻度值,对指针对应刻度值位置进行了识别,并分割提取出刻度线所指的字符区域。研究了针对指针万用表表壳图像的字符切分和粘连字符的处理方法。
2、研究了指针式万用表表壳图像的字符识别和类别识别方法:
采用模板匹配实现了字符识别,详细介绍了模板的制作以及字符模板的匹配计算方法。针对指针万用表表壳图像的特点,研究了字符的类别识别方法:区域颜色识别方法和弧度线包括刻度线的范围识别方法,并设计实现了字符识别算法和字符类别识别算法。
3、设计实现指针万用表表壳图像处理系统,实现了自动识别,具有一定的实用价值:
通过Matlab7.0软件实现了上述方法和算法,对指针万用表表壳图像进行了仿真分析和处理。设计了表壳图像处理系统的软件,通过实际表壳图像处理,取得了较好的效果。
The automatic processing and recognition of the shell image of pointer multi-meter is one of an important component for the automatic calibration devices which is based on machine vision. In this thesis, the technology of machine vision and image processing has been applied to process the automatic processing and recognition of the shell image of pointer multi-meter, which is stressed on image segmentation, tilt correction and character segmentation and recognition.
In the aspect of segmentation, tilt correction and character recognition, the technology has become mature and it has acquired great achievements all around the world. However, the research of the shell image of the pointer multi-meter is inadequate and the analytic documents about it are also seldom. As a result, it is of great significant to research on the shell image processing.
This thesis mainly did something and got some achievements as follows:
1. Research the pre-processing and segmentation of the shell image of the pointer multi-meter.
Research the pre-processing of the shell image of the multi-meter, segment and extract the pointer of the switch to multi-meter measuring range and the compass display of the black disk. Design algorithms to realize accurate image inpainting for the compass display. Segment and extract the graduated values round the black disk, then recognize the location of the pointer which relevant to the graduated value. At last, extract the text region. Research the character segmentation and character conglutination for the shell image of the point multi-meter.
2. Research the text recognition and classification recognition of the shell image of the pointer multi-meter.
Realize the text recognition through template matching. Introduce the methods of how to make the template and how to calculate the template matching. According to the feature of the shell image of the pointer multi-meter, research the character category recognition: the method of regional color recognition and the method of the range of curve contain the compass display. Then design and realize the algorithm of the text recognition and character category recognition.
3. Design and realize the image processing system of the shell image of pointer multi-meter.
Through using the software of Matlab7.0, good results have acquired from the experiment of the automatic processing and recognition of the shell image of pointer multi-meter. The algorithms and methods have been realized and applied to the automatic calibration system.
引文
[1]Zhang Yujin.A Review of Recent Evaluation Methods for image Segmentation[A].Malafsia:International Symposinm on Signal Processing and Its Application[C].2001:13-16.
[2]韩思奇,王蕾.图像分割的阈值法综述[J].系统工程与电子技术,2002,24(6):91-102.
[3]Kyong-Ho Kim,Sung-Li Chien,~ong-Bum Lee,Jong Min Lee.A Study on Analog and Digital Meter Recognition Based on Image Processing Technique.Joural of the Korean Institute of Telematics and Electronics,1995,9(32):79-94.
[4]Sablatnig,Robert,Kropatsch,Walter G.Automatic Reading of Analog Display Instruments Conference on Pattern Recognition,1994,1:794-797.
[5]王三武,戴亚文,景仁坤.复杂仪表的图像自动读数识别方法[J].武汉理工大学学报(信息与管理工程版),2003,6(25):76-78.
[6]张艰,赵宇明,陈德权.分时电表表盘数字读数的自动识别系统[J].计算机工程,2005,5:178-180.
[7]陈世伟,李世平等.一种针对指针式仪表表盘图像的分割方法[J].激光与红外,2006,1:78-80.
[8]任光龙.指针式仪表图像的快速分割法研究[J].计算机工程与设计,第26卷第3期,2005.3:790-792.
[9]李盛阳,叶梧,冯穗利.基于Hough变换的视觉识别方法在仪表自动检测中的应用研究[J].科学技术与工程,第5卷第2期,2005,1:82-85.
[10]杨卫平,李吉成,沈振康.车牌目标的自动定位技术[J].中国图像图形学报,第7卷(A 版)第8期2002,8:835-839.
[11]阎建国,高亮,卢京潮.图像处理技术在车牌识别中的应用[J].电子技术应用,2000,26(1):17-18.
[12]林纯青,徐立亚,戚飞虎.汽车图像中字符目标的提取算法[J].上海交通大学学报,1998,32(10):1-3.
[13]赵雪春,戚飞虎.基于彩色分割的车牌自动识别技术[J].上海交通大学学报,1998,
32(10):4-9.
[14]章东平,刘济林,罗义军.车辆牌照字符的提取[J].电路与系统学报,2003,8(4):73-76.
[15]权炜,郑南宁,贾新春.复杂背景下的车辆牌照字符提取方法研究[J].信息与控制,2002,31(1):25-29.
[16]陈松,姚伯威.模式识别技术在仪器仪表数字显示系统上的应用[J].中国测试技术,2005,31(2):73-74.
[17]许录平.数字图像处理[M].北京:科学出版社.2007.10,211-216.
[18]傅德胜,授益禾.图形图像处理学[M].南京:东南大学出版社.2002.1,334-341.
[19]陈传波,金先级.数字图像处理[M].北京:机械工业共版社.2004.7,186-187.
[20]姚敏.数字图像处理[M].北京:机械工业出版社.2006.1,22-24.
[21]龚声蓉,刘纯平,王强.数字图像处理与分析[M].北京:清华大学出版社.2006.7,206-207.
[22]ANIL K.JAIN.数字图像处理基础[M].北京:清华大学出版社.2006.11,193-194.
[23]钟志光,卢军,刘伟荣.Visual C++.NET数字图像处理实例与解析[M].北京:清华大学出版社.2003.6,246-254.
[24]朗锐.数字图像处理 Visual C++实现[M].北京:北京希望电子出版社.2002.12,337-344.
[25]杨淑莹.VC++数字图像处理程序设计[M].北京:清华大学出版社.2005.1,264-278.
[26]于殿泓.图像检测与处理技术[M].西安:西安电子科技大学出版社.2006,12,182-188.
[27]何东健.数字图像处理[M].西安:西安电子科技大学出版社.2003.7,115-120.
[28]富煜清,沈巍,黄心哗.汽车牌照的提取方法研究[J].模式识别与人工智能,2000,43(3):345-348.
[29]涂其远,吴建华,万国金.动态阈值结合全局阈值对图像进行分割[J].南昌大学学报(工科版),2002,24(1):37-40.
[30]戴亚文,王三武,吴小兰.基于图像的复杂仪表计数识别[J].仪表技术,2003,2:11-12.
[31]王妹华,李佐,蔡士杰.基于直线连续性的页面倾斜检测与校正[J].计算机辅助设计与图形学学报,2001,13(8):736-741.
[32]严义,包健.一种实用视觉识别的仪表自动检定系统[J].仪表技术与传感器, 2002,1:28-30。
[33]韩思奇,王蕾.图像分割的阈值法综述[J].系统工程与电子技术,2002,24(6):91-102.
[34]高朝飞,黄卫.基于彩色图像车牌分割研究[J].公路交通科技,2004,8:115-117.
[35]王志勇,邓达,余英林.分形技术在彩色纹理分割中的应用[J].华南理工大学学报,1998.10:64-70.
[36]陈智斌,黎邵发,余棉水.车辆牌照定位算法研究[J].计算机工程与设计,2006,11:4058-4059.
[37]韩永强,李世祥.汽车牌照子图像的定位算法[J].微型电脑应用,1999,3:14-16.
[38]傅一平,李志能,袁丁.基于HSI空间的颜色算法提牌照识别的性能[J].计算机工程与设计,2004,25(5):703-707.
[39]沈会良,李志能.基于CCD的汽车牌照自动识别系统[J].光电工程,2000,27(4):60-71.
[40]郭勇,吴乐南.行驶车辆的牌照识别系统[J].电子工程师,2000,11:37-41.
[41]汪惠,鲍旭东,罗立民.车轮序列号自动识别系统[J].测控技术,2002,21(7):23-25.
[42]陈寅鹏,丁晓青.复杂车辆图像中的车牌定位与字符分割方法[J].红外与激光工程,2004,33(1):29-33.
[43]陈黎,黄心汉.基于聚类分析的车牌字符分割方法[J].计算机工程与应用,2002(6):221-222.
[44]范玮琦,穆长江.一种基于汉字结构特征的车牌照字符分割方法[J].仪器仪表学报,2003,24(4):472-474.
[45]李文举,梁德群.质量退化的车牌字符分割方法[J].计算机辅助设计与图形学学报,2004,6(5):697-700.
[46]韩智广,老松杨.车牌分割与矫正[J].计算机工程与应用,2003,9:210-212.
[47]余文勇,周祖德,陈幼平.一种高速印刷品缺陷在线检测系统[J].华中科技大学学报(自然科学版),2006,34(6):80-83.
[48]韩斌,刘以安,王士同.基于图像处理的印刷缺陷计算机自动检测[J].自动化技术与应用,2002,21(3)37-38.
[49]谢勇,王耀南,彭涛.基于机器视觉印品缺陷检测的滤波算法[J].湖南大学学报(自 然科学版),2005,32(4):53-57.
[50]罗中兵,付忠良,阮波.一种改进的模板匹配算法[J].计算机应用,2002,22(3):28-30.
[51]王耀革'赵亚宏,陈星.基于数学形态学的点状石头目标检测[J].测绘学院学报,2005,22(1):24-26.
[52]丁明跃,王宁军,彭嘉雄.一类快速匹配算法的模拟与比较[J].数据采集与处理,1990,5(2):41-48.
[53]景晓军,蔡安妮,孙景鳌.一种基于二维最大类间方差的图像分割算法[J].通信学报,2001,22(4):71-76.
[54]郑南宁,张两宁,戴莹,等.行驶车辆牌照自动识别系统[J].西安交通大学学报,1991,25(1):43-54.
[55]刘晓静.汽车牌照自动识别技术研究[J].南京航空航天大学学报,1998,30(5):573-576.
[56]梁吉,蒋式勤,沈立纬.视觉检测系统及其应用[J].微计算机信息,2003,19(12):44-45
[57]Newman T S,Jain A K.A survey of automated visual inspection Cornputer Vision and Image Understanding,1995,61(2):213-262.
[58]Kapur J N,Sahoo P K,WongA K C.A new method for gray-level picture thresholding using the entropy of the histogram.ComputerVision,Graphics,and Image Processing,1985,29(3):273-285.
[59]Shih M Y,Shishido A,Khoo[C].All optical Image Processing by Means of a Photosensitive Non-linear Liquid -crystal Film:Edge Enhancement and Image Addition Subtraction.Optics Ietters.2001,26(15):1140-1142.
[60]Y Lu,C L Tan.Word Spotting in Chinese Document Images Without Layout Analysis [C].Proc.of the 16th International Conference on Pattern Recognition.2002.57-60.
[61]Que Dashun,Liu Minghui.Algorithm Research on the Detection and Orientation of Vehicle license plate Image Based on Wavelet[J].Transportation Science&Technology.2005,209(2):91-93.
[62]Zeng Xian gui,Li Shaofa,Zuo Wenming.Color and Templates Matching-based Face Detection[J].Journal of Guangzhou Maritime College,2003,21(2):37-38.
[63] Shridhar M, Miller J W V, et al. Recognition of License Plate Images: Issues and Perspectives[C]. Proceedings of the 5th International Conference on Document Analysis and Recognition. 1999, 17-20.
[64] Briechle K, Hanebeck U D. Template matching using fast normalized cross corelation [C]. Orlando, FL: Proceedings of SPIE, Optical Pauem Recognition XII, 2001: 95-102.
[65] Min-Seok Choi, Whoi-Yul Kim. A novel two stage template matching method for rotaion and illumination invariance[J]. Pattern Recognition, 2002, 35: 119-129.
[66] Farhan Ullah, Shun' ichi Kaneko. Using orientation codes for rotation invariant template matching[J]. Pattern Recognition, 2004, 37: 20 1-209.
[67] Fahmy M. M. M. , Computer Vision Application to Automatic Numberplate Recognition. Proceeding of 26th[D]. International Symposium on Automotive Technology and Automation, Aachen, Germany, 1993: 625—633.
[68] AtamanE, AatreVK, WongKM. A fast method for real-time median filtering[J]. IEEE Transactions on Acoustics Speech and Signal Processing ,1980, 28(4): 415-421.
[69] Castleman K R. Digital image processing [M]. Beijing: Tsinghua University Press, 1998: 492-495.
[2]韩思奇,王蕾.图像分割的阈值法综述[J].系统工程与电子技术,2002,24(6):91-102.
[3]Kyong-Ho Kim,Sung-Li Chien,~ong-Bum Lee,Jong Min Lee.A Study on Analog and Digital Meter Recognition Based on Image Processing Technique.Joural of the Korean Institute of Telematics and Electronics,1995,9(32):79-94.
[4]Sablatnig,Robert,Kropatsch,Walter G.Automatic Reading of Analog Display Instruments Conference on Pattern Recognition,1994,1:794-797.
[5]王三武,戴亚文,景仁坤.复杂仪表的图像自动读数识别方法[J].武汉理工大学学报(信息与管理工程版),2003,6(25):76-78.
[6]张艰,赵宇明,陈德权.分时电表表盘数字读数的自动识别系统[J].计算机工程,2005,5:178-180.
[7]陈世伟,李世平等.一种针对指针式仪表表盘图像的分割方法[J].激光与红外,2006,1:78-80.
[8]任光龙.指针式仪表图像的快速分割法研究[J].计算机工程与设计,第26卷第3期,2005.3:790-792.
[9]李盛阳,叶梧,冯穗利.基于Hough变换的视觉识别方法在仪表自动检测中的应用研究[J].科学技术与工程,第5卷第2期,2005,1:82-85.
[10]杨卫平,李吉成,沈振康.车牌目标的自动定位技术[J].中国图像图形学报,第7卷(A 版)第8期2002,8:835-839.
[11]阎建国,高亮,卢京潮.图像处理技术在车牌识别中的应用[J].电子技术应用,2000,26(1):17-18.
[12]林纯青,徐立亚,戚飞虎.汽车图像中字符目标的提取算法[J].上海交通大学学报,1998,32(10):1-3.
[13]赵雪春,戚飞虎.基于彩色分割的车牌自动识别技术[J].上海交通大学学报,1998,
32(10):4-9.
[14]章东平,刘济林,罗义军.车辆牌照字符的提取[J].电路与系统学报,2003,8(4):73-76.
[15]权炜,郑南宁,贾新春.复杂背景下的车辆牌照字符提取方法研究[J].信息与控制,2002,31(1):25-29.
[16]陈松,姚伯威.模式识别技术在仪器仪表数字显示系统上的应用[J].中国测试技术,2005,31(2):73-74.
[17]许录平.数字图像处理[M].北京:科学出版社.2007.10,211-216.
[18]傅德胜,授益禾.图形图像处理学[M].南京:东南大学出版社.2002.1,334-341.
[19]陈传波,金先级.数字图像处理[M].北京:机械工业共版社.2004.7,186-187.
[20]姚敏.数字图像处理[M].北京:机械工业出版社.2006.1,22-24.
[21]龚声蓉,刘纯平,王强.数字图像处理与分析[M].北京:清华大学出版社.2006.7,206-207.
[22]ANIL K.JAIN.数字图像处理基础[M].北京:清华大学出版社.2006.11,193-194.
[23]钟志光,卢军,刘伟荣.Visual C++.NET数字图像处理实例与解析[M].北京:清华大学出版社.2003.6,246-254.
[24]朗锐.数字图像处理 Visual C++实现[M].北京:北京希望电子出版社.2002.12,337-344.
[25]杨淑莹.VC++数字图像处理程序设计[M].北京:清华大学出版社.2005.1,264-278.
[26]于殿泓.图像检测与处理技术[M].西安:西安电子科技大学出版社.2006,12,182-188.
[27]何东健.数字图像处理[M].西安:西安电子科技大学出版社.2003.7,115-120.
[28]富煜清,沈巍,黄心哗.汽车牌照的提取方法研究[J].模式识别与人工智能,2000,43(3):345-348.
[29]涂其远,吴建华,万国金.动态阈值结合全局阈值对图像进行分割[J].南昌大学学报(工科版),2002,24(1):37-40.
[30]戴亚文,王三武,吴小兰.基于图像的复杂仪表计数识别[J].仪表技术,2003,2:11-12.
[31]王妹华,李佐,蔡士杰.基于直线连续性的页面倾斜检测与校正[J].计算机辅助设计与图形学学报,2001,13(8):736-741.
[32]严义,包健.一种实用视觉识别的仪表自动检定系统[J].仪表技术与传感器, 2002,1:28-30。
[33]韩思奇,王蕾.图像分割的阈值法综述[J].系统工程与电子技术,2002,24(6):91-102.
[34]高朝飞,黄卫.基于彩色图像车牌分割研究[J].公路交通科技,2004,8:115-117.
[35]王志勇,邓达,余英林.分形技术在彩色纹理分割中的应用[J].华南理工大学学报,1998.10:64-70.
[36]陈智斌,黎邵发,余棉水.车辆牌照定位算法研究[J].计算机工程与设计,2006,11:4058-4059.
[37]韩永强,李世祥.汽车牌照子图像的定位算法[J].微型电脑应用,1999,3:14-16.
[38]傅一平,李志能,袁丁.基于HSI空间的颜色算法提牌照识别的性能[J].计算机工程与设计,2004,25(5):703-707.
[39]沈会良,李志能.基于CCD的汽车牌照自动识别系统[J].光电工程,2000,27(4):60-71.
[40]郭勇,吴乐南.行驶车辆的牌照识别系统[J].电子工程师,2000,11:37-41.
[41]汪惠,鲍旭东,罗立民.车轮序列号自动识别系统[J].测控技术,2002,21(7):23-25.
[42]陈寅鹏,丁晓青.复杂车辆图像中的车牌定位与字符分割方法[J].红外与激光工程,2004,33(1):29-33.
[43]陈黎,黄心汉.基于聚类分析的车牌字符分割方法[J].计算机工程与应用,2002(6):221-222.
[44]范玮琦,穆长江.一种基于汉字结构特征的车牌照字符分割方法[J].仪器仪表学报,2003,24(4):472-474.
[45]李文举,梁德群.质量退化的车牌字符分割方法[J].计算机辅助设计与图形学学报,2004,6(5):697-700.
[46]韩智广,老松杨.车牌分割与矫正[J].计算机工程与应用,2003,9:210-212.
[47]余文勇,周祖德,陈幼平.一种高速印刷品缺陷在线检测系统[J].华中科技大学学报(自然科学版),2006,34(6):80-83.
[48]韩斌,刘以安,王士同.基于图像处理的印刷缺陷计算机自动检测[J].自动化技术与应用,2002,21(3)37-38.
[49]谢勇,王耀南,彭涛.基于机器视觉印品缺陷检测的滤波算法[J].湖南大学学报(自 然科学版),2005,32(4):53-57.
[50]罗中兵,付忠良,阮波.一种改进的模板匹配算法[J].计算机应用,2002,22(3):28-30.
[51]王耀革'赵亚宏,陈星.基于数学形态学的点状石头目标检测[J].测绘学院学报,2005,22(1):24-26.
[52]丁明跃,王宁军,彭嘉雄.一类快速匹配算法的模拟与比较[J].数据采集与处理,1990,5(2):41-48.
[53]景晓军,蔡安妮,孙景鳌.一种基于二维最大类间方差的图像分割算法[J].通信学报,2001,22(4):71-76.
[54]郑南宁,张两宁,戴莹,等.行驶车辆牌照自动识别系统[J].西安交通大学学报,1991,25(1):43-54.
[55]刘晓静.汽车牌照自动识别技术研究[J].南京航空航天大学学报,1998,30(5):573-576.
[56]梁吉,蒋式勤,沈立纬.视觉检测系统及其应用[J].微计算机信息,2003,19(12):44-45
[57]Newman T S,Jain A K.A survey of automated visual inspection Cornputer Vision and Image Understanding,1995,61(2):213-262.
[58]Kapur J N,Sahoo P K,WongA K C.A new method for gray-level picture thresholding using the entropy of the histogram.ComputerVision,Graphics,and Image Processing,1985,29(3):273-285.
[59]Shih M Y,Shishido A,Khoo[C].All optical Image Processing by Means of a Photosensitive Non-linear Liquid -crystal Film:Edge Enhancement and Image Addition Subtraction.Optics Ietters.2001,26(15):1140-1142.
[60]Y Lu,C L Tan.Word Spotting in Chinese Document Images Without Layout Analysis [C].Proc.of the 16th International Conference on Pattern Recognition.2002.57-60.
[61]Que Dashun,Liu Minghui.Algorithm Research on the Detection and Orientation of Vehicle license plate Image Based on Wavelet[J].Transportation Science&Technology.2005,209(2):91-93.
[62]Zeng Xian gui,Li Shaofa,Zuo Wenming.Color and Templates Matching-based Face Detection[J].Journal of Guangzhou Maritime College,2003,21(2):37-38.
[63] Shridhar M, Miller J W V, et al. Recognition of License Plate Images: Issues and Perspectives[C]. Proceedings of the 5th International Conference on Document Analysis and Recognition. 1999, 17-20.
[64] Briechle K, Hanebeck U D. Template matching using fast normalized cross corelation [C]. Orlando, FL: Proceedings of SPIE, Optical Pauem Recognition XII, 2001: 95-102.
[65] Min-Seok Choi, Whoi-Yul Kim. A novel two stage template matching method for rotaion and illumination invariance[J]. Pattern Recognition, 2002, 35: 119-129.
[66] Farhan Ullah, Shun' ichi Kaneko. Using orientation codes for rotation invariant template matching[J]. Pattern Recognition, 2004, 37: 20 1-209.
[67] Fahmy M. M. M. , Computer Vision Application to Automatic Numberplate Recognition. Proceeding of 26th[D]. International Symposium on Automotive Technology and Automation, Aachen, Germany, 1993: 625—633.
[68] AtamanE, AatreVK, WongKM. A fast method for real-time median filtering[J]. IEEE Transactions on Acoustics Speech and Signal Processing ,1980, 28(4): 415-421.
[69] Castleman K R. Digital image processing [M]. Beijing: Tsinghua University Press, 1998: 492-495.