金融票据圆形印鉴真伪识别方法研究
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摘要
印鉴作为标识身份的重要依据得到了广泛应用,特别是用于金融票据上,作为其法律有效性的最重要特征。目前印鉴的真伪辨别主要是通过人工“折角核印”,识别精度不能保证,伪造印鉴进行诈骗的犯罪活动时有发生,因此,印鉴真伪自动识别系统的研究具有重要意义。本文研究基于嵌入式系统技术的印鉴真伪检验系统,从系统的角度研究了金融票据圆形印鉴真伪识别方法,提出了从图像校正、预处理、特征提取到最终识别等一套完整的印鉴真伪识别算法,并设计、制作了基于高速DSP的嵌入式图像采集、处理系统,进行了圆形印鉴图像识别算法的实验和测试。主要内容包括:
1、图像畸变校正研究。分析了图像采集过程中畸变的成因,建立了畸变和复原模型,根据该模型对畸变图像进行复原校正研究。对由于LED光源照度分布不均匀造成的图像非均匀性畸变,结合CMOS图像传感器的输出特性,提出了分段两点法对图像进行校正。对于图像的几何失真畸变,利用多项式拟合的网格模板图像,得到系统的几何畸变参数,然后利用该参数对图像进行几何校正和灰度插值,得到复原的图像。
2、圆形印鉴ROI(感兴趣区域)提取算法研究和实现。以红色圆形印鉴为例,利用票据图像的彩色信息进行处理,把图像转换为灰度图像后,突出了原来的红色分量。再利用平滑卷积确定五角星上的一个像素A。以A为中心,半径为R(R大于印鉴半径)选择多个像素点。然后分别从各个象素点向A进行搜索,得到印鉴边框上多个像素点Bi,对其分析后保留标准差较小的m个点,忽略其他点。利用三点定圆法计算出印鉴的圆心和半径,提取到印鉴的ROI。
3、计算印鉴偏转角度的研究与计算。把圆形印鉴ROI从圆心向其外边框进行投影,得到一维特征数据,在不同角度下分别计算SS(Sample Seal)和MS(Model Seal)投影之间的相关性,相似度最大时的相对角度就是所求偏转角度。实验结果表明,其定位精度≤0 .5°。
4、圆形印鉴的真伪识别算法研究。在对SS和MS进行位置和角度精确配准的基础上,检测圆形印鉴的区域边缘。确定SS和MS之间的边缘对应关系后,采用改进Hausdorff距离计算其相似性,将其作为印鉴的真伪识别特征。为减小印鉴的质量对识别结果的影响,把边缘对应关系作为印鉴质量测度,采用神经网络方法进行综合分析、判别。实验表明,其识别率大于95%。
5、算法在基于DSP的嵌入式系统中的实验与测试。采用高速的DSP作为主处理器,设计、制作了嵌入式圆形印鉴图像采集、处理系统,编写了实验和测试软件,对识别算法在DSP中进行实验和测试。实验结果表明,该算法可以在基于DSP的嵌入式系统中有效的进行圆形印鉴真伪识别,识别速度为1秒左右。在验证算法有效性的同时,也分析了算法各环节的处理速度,为算法的进一步优化和改进提供了依据。
Seal plays an essential role in identifying entity status. Particularly, as a usual method, it proves law effect of financial notes stamped with seal. In most cases, the recognition of seal is completed with folding seal in diagonal and aligning manually. The accuracy of recognition result depends on man-made decision and grift crimes always happen with counterfeit financial seal, which are the reasons we manage to reaserch another unique method to solve this problem. Hence, A seal verification system is studied in this paper, based on embedded system technology. The recognition method for circle seal of finance notes is discovered from the view of system, and a series of recognition algorithms from image adjustment, preprocess, feature extraction to estimation are proposed. Based on high-speed DSP, the embedded image acquiring and processing system is designed and fabricated. Experimentally, the recognition algorithms is carried out.
The followings are the main research contents:
1. Image aberration adjustment. The factors leading to aberration are analyzed, and a model of aberration and reconstruction is established and is used for reconstruction adjustment of aberrated image. A section two points image adjustment method is proposed to solve the nonuniform aberration resulting from luminance distribution of LED light source, combining with the output character of CMOS image sensor. As far as geometric aberration is concerned, mesh template image obtained with polynomial fitting is used for geometric aberration parameter determination of system and then the parameters are used in geometric adjustment and gray interpolation for image reconstruction.
2. Research and implement of ROI (Region of Interesting) extraction algorithm of circle seal. The red seal is selected as research sample. This seal is processed by color information of note image. For example, the color image is transferred to gray image to bring red component of original image into prominence firstly. Then a special pixel of pentagram is obtained with flatness convolution and defined as A. With the pixel A as center, multiple searches having direction to A are carried out until eight pixels in seal frame are obtained, these pixels are defined as Bi. Among of them, the pixels having smaller standard deviation will be kept while the others ignored. The circle center and radius of seal are calculated with three point determination method of circle, and then the ROI is extracted.
3. Reaserch and caculation of measurement of seal orientation deflexion. Through the projection of seal ROI from its center to outer frame, a group of one dimension feature data is obtained. The correlation is calculated, between SS (Sample Seal and Model Seal) under different orientations. And then, we can determine the orientation with maximize the correlation. The experiment results demonstrate that the precision is≤0 .5°
4. Authenticity recognition arithmetic of circle seal. The edge features of seal are adopted as recognition features, on basis of position and orientation precise registration of SS and MS. The corresponding relation, between edges of SS and MS, is determined and then similarity measure is calculated with improved Hausdorff distance. In order to alleviate the effect of seal quality on edge similarity, the corresponding relation is used as the measure of seal quality and artificial neural network is adopted in analyzing and estimation. Experiment shows that the recognition probability is up to 95%.
5. Experiments and evaluation of recognition algorithm in DSP embeded system. This image acquirement and process system based on high speed DSP is designed and fabricated. Then the function measurement and performance of algorithm are carried out, which validate the system effectivity. The time consuming of each section of algorithm is analyzed, which provides the cornerstone for further optimum and improvement.
1、图像畸变校正研究。分析了图像采集过程中畸变的成因,建立了畸变和复原模型,根据该模型对畸变图像进行复原校正研究。对由于LED光源照度分布不均匀造成的图像非均匀性畸变,结合CMOS图像传感器的输出特性,提出了分段两点法对图像进行校正。对于图像的几何失真畸变,利用多项式拟合的网格模板图像,得到系统的几何畸变参数,然后利用该参数对图像进行几何校正和灰度插值,得到复原的图像。
2、圆形印鉴ROI(感兴趣区域)提取算法研究和实现。以红色圆形印鉴为例,利用票据图像的彩色信息进行处理,把图像转换为灰度图像后,突出了原来的红色分量。再利用平滑卷积确定五角星上的一个像素A。以A为中心,半径为R(R大于印鉴半径)选择多个像素点。然后分别从各个象素点向A进行搜索,得到印鉴边框上多个像素点Bi,对其分析后保留标准差较小的m个点,忽略其他点。利用三点定圆法计算出印鉴的圆心和半径,提取到印鉴的ROI。
3、计算印鉴偏转角度的研究与计算。把圆形印鉴ROI从圆心向其外边框进行投影,得到一维特征数据,在不同角度下分别计算SS(Sample Seal)和MS(Model Seal)投影之间的相关性,相似度最大时的相对角度就是所求偏转角度。实验结果表明,其定位精度≤0 .5°。
4、圆形印鉴的真伪识别算法研究。在对SS和MS进行位置和角度精确配准的基础上,检测圆形印鉴的区域边缘。确定SS和MS之间的边缘对应关系后,采用改进Hausdorff距离计算其相似性,将其作为印鉴的真伪识别特征。为减小印鉴的质量对识别结果的影响,把边缘对应关系作为印鉴质量测度,采用神经网络方法进行综合分析、判别。实验表明,其识别率大于95%。
5、算法在基于DSP的嵌入式系统中的实验与测试。采用高速的DSP作为主处理器,设计、制作了嵌入式圆形印鉴图像采集、处理系统,编写了实验和测试软件,对识别算法在DSP中进行实验和测试。实验结果表明,该算法可以在基于DSP的嵌入式系统中有效的进行圆形印鉴真伪识别,识别速度为1秒左右。在验证算法有效性的同时,也分析了算法各环节的处理速度,为算法的进一步优化和改进提供了依据。
Seal plays an essential role in identifying entity status. Particularly, as a usual method, it proves law effect of financial notes stamped with seal. In most cases, the recognition of seal is completed with folding seal in diagonal and aligning manually. The accuracy of recognition result depends on man-made decision and grift crimes always happen with counterfeit financial seal, which are the reasons we manage to reaserch another unique method to solve this problem. Hence, A seal verification system is studied in this paper, based on embedded system technology. The recognition method for circle seal of finance notes is discovered from the view of system, and a series of recognition algorithms from image adjustment, preprocess, feature extraction to estimation are proposed. Based on high-speed DSP, the embedded image acquiring and processing system is designed and fabricated. Experimentally, the recognition algorithms is carried out.
The followings are the main research contents:
1. Image aberration adjustment. The factors leading to aberration are analyzed, and a model of aberration and reconstruction is established and is used for reconstruction adjustment of aberrated image. A section two points image adjustment method is proposed to solve the nonuniform aberration resulting from luminance distribution of LED light source, combining with the output character of CMOS image sensor. As far as geometric aberration is concerned, mesh template image obtained with polynomial fitting is used for geometric aberration parameter determination of system and then the parameters are used in geometric adjustment and gray interpolation for image reconstruction.
2. Research and implement of ROI (Region of Interesting) extraction algorithm of circle seal. The red seal is selected as research sample. This seal is processed by color information of note image. For example, the color image is transferred to gray image to bring red component of original image into prominence firstly. Then a special pixel of pentagram is obtained with flatness convolution and defined as A. With the pixel A as center, multiple searches having direction to A are carried out until eight pixels in seal frame are obtained, these pixels are defined as Bi. Among of them, the pixels having smaller standard deviation will be kept while the others ignored. The circle center and radius of seal are calculated with three point determination method of circle, and then the ROI is extracted.
3. Reaserch and caculation of measurement of seal orientation deflexion. Through the projection of seal ROI from its center to outer frame, a group of one dimension feature data is obtained. The correlation is calculated, between SS (Sample Seal and Model Seal) under different orientations. And then, we can determine the orientation with maximize the correlation. The experiment results demonstrate that the precision is≤0 .5°
4. Authenticity recognition arithmetic of circle seal. The edge features of seal are adopted as recognition features, on basis of position and orientation precise registration of SS and MS. The corresponding relation, between edges of SS and MS, is determined and then similarity measure is calculated with improved Hausdorff distance. In order to alleviate the effect of seal quality on edge similarity, the corresponding relation is used as the measure of seal quality and artificial neural network is adopted in analyzing and estimation. Experiment shows that the recognition probability is up to 95%.
5. Experiments and evaluation of recognition algorithm in DSP embeded system. This image acquirement and process system based on high speed DSP is designed and fabricated. Then the function measurement and performance of algorithm are carried out, which validate the system effectivity. The time consuming of each section of algorithm is analyzed, which provides the cornerstone for further optimum and improvement.
引文
[1]. 顾惠敏. 印鉴鉴赏与收藏[M]. 上海:上海书店出版社, 1996.
[2]. 蔡国声. 印章三千年[M]. 上海:上海文化出版社, 1996.
[3]. 刘一闻. 印章[M].上海:上海古籍出版社, 1995.
[4]. 孙慰祖. 印章[M]. 上海:上海人民美术出版社, 1998.
[5]. 国务院关于国家行政机关和企业、事业单位印章的规定. http://bl.ningbo.gov.cn/news_read.php?id=9567&catg_code=news-01-05-02-18-01,2002-12-19/2007-4-8.
[6]. 中华人民共和国票据法. http://www.china.com.cn/chinese/law/647634.htm, 2004-8-9/2007-4-6.
[7]. 银行账户管理办法.http://www.gxu.edu.cn/administration/cwc/cwwj/zhgl/1114.htm,1994-10-09/2007-05-04.
[8]. 应严防伪造客户印鉴诈骗案.http://fol.math.sdu.edu.cn/jrfy/content/content.php?id=60&tb=dxal, 2002-05-11/2007-05-04.
[9]. 西安一妇女私刻“国家体育总局”印章诈骗 800 万. http://www.jxgdw.com/jxgd/news/shxw/userobject1ai598645.html, 2003-4-29/2007-04-08.
[10]. 北京三金融巨骗私刻首钢印章诈骗亿元被判极刑.http://china.eastday.com/epublish/gb/paper148/20030513/class014800003/hwz942073.htm,2003-05-13/2007-05-03.
[11]. 诈骗团伙伪造印鉴骗得 3500 万巨款.http://ent.caiku.com/pic/shwx/2006-5/31/2006053109485649.htm,2006-05-31/2007-07-20.
[12]. 揭密支票诈骗第一案.http://www.cctv.com/program/jjbxs/20050120/101298.shtml,2005-01-19/2007-07-20.
[13]. 印章防伪制作技术的发展原理及现状[J].中国防伪报道, 2005(1):3-4.
[14]. K Ueda, Y Nakamura. Automatic verification of seal impression pattern[J], Proc.7th Inter Conf Pattern Recognition(ICPR), 1984(2): 1019-1021.
[15]. Fan T J, Tsai W H. Automatic Chinese seal identification[J]. Computer Vision Graphics and Image Processing, 1984, 25(2): 311-330.
[16]. C M Chen. W H Tsai. Automatic identification of Chinese seal imprint with arbitrary or partial shapes[J]. Computer Processing Chinese OrientalLanguages, 1986,22(6): 216-233.
[17]. S Lee, J H Kim. Unconstrained seal imprint verification using attributed stroke graph matching[J]. Pattern Recognition, 1989, 22(6): 653-664.
[18]. Michael Yang-Min Fuh. Medial axis transformation and its application toseal imprint verification[D]. USA:Temple University, 2001.
[19]. Wen Gao, Sheng fu dong ,Xinlin Chen.A system for automatic Chinese seal imprint verification[J]. Proceedings of the Third International Confere-nce on Document Analysis and Recognition. 1995, pp. 660-664.
[20]. 陈燕新, 李卫东, 戚飞虎. 基于边缘和模板匹配的印鉴自动鉴别方法[J],上海交通大学学报,1998, 32(4): 44-48.
[21]. 杨新军, 王肇圻, 刘唯一, 母国光. 基于环投影模板匹配的印鉴鉴别法[J], 光电子.激光, 2001, 12(11): 1177-1181.
[22]. 耿建玲, 陈纯, 陈耀武, 黄轶伦.基于小波多尺度分解的印鉴图像配准[J], 计算机工程与应用,2006(1): 34-36.
[23]. Qing Hu, JingYu Yang, Qian Zhang, et al. An automatic seal imprint verification approach[J]. Pattern Recognition, 1995, 28(8):1251-1266.
[24]. Q. Hu, J. Yang, Q. Zhang and K. Liu, A seal-imprint verification system[J], Actaautomat. Sin. 1991, 17(6): 696-704.
[25]. J. Yang, Q. Hu, Q. Zhang and K. Liu, Automatic seal recognition for banking[J], Financial Comput. China 4, 1990: 14-16.
[26]. 胡庆, 杨静宇, 张黔, 等. 基于知识的印鉴鉴别方法[J]. 自动化学报,1991, 17(6): 696-704.
[27]. 张先萌, 罗安玉, 王翔, 等. 用矩不变量实现印鉴自动识别[J]. 电子学报, 1995, 23(4): 100-103.
[28]. M.K.Hu Visual pattern recognition by moment invariants[J]. IRE Trans. Inf. Theory, 1962: 179-187.
[29]. 电子验印方案. http://www.founderorder.com/product/productDetail.aspx?bh=42,/2007-05-08.
[30]. 引领验印新浪潮.http://www.hisuntech.com/aboutus/kanwu/2yingxiang03.htm,/2007-07-20.
[31]. 北大青鸟验印系统.http://www.jbbis.com.cn/solution/finance/diannaoyanyin.htm 2006.5.14/2007-05-14.
[32]. 金联安印章防伪鉴别系统.http://www.jla.com.cn/product1.htm,/2007-07-21.
[33]. Rafael C.Gonzalez,Richard E.Woods,阮秋琦,阮宇智等译.数字图像处理[M].北京:电子工业出版社.
[34]. 朱森良.计算机视觉[M].浙江:浙江大学出版社,1997.
[35]. 刘富强.数字视频监控系统开发及应用[M].北京:机械工业出版社,2003.
[36]. 章毓晋.图象分割[M].北京:科学出版社,2001.
[37]. 陈鲤江.基于彩色图像识别的目标跟踪研究[D].天津:南开大学博士生论文.2004.
[38]. 王庆有.CCD 应用技术[M].天津:天津大学出版社,2000.
[39]. 王庆有.图像传感器应用技术[M].北京:电子工业出版社,2003.
[40]. Blanksby A J,Loinaz M J.Performance Analysis of a Color CMOS Phot-ogate Image Sensor[J].IEEE Transactions,2000,47(1):55-64.
[41]. B.E.Bayer,Color Imaging Array,US Patent 3,971,065-1976.
[42]. M M Sondhi.Image restoration:the removal of spatially invariant degradat-ions[J].Proceedings IEEE,1967,60:842-853.
[43]. Andrews H C,Hunt B R.Digital image restoration[M].Englewood Clifs,New Jersey,USA:Prentice-Hall,1977.
[44]. A Rosenfeld,A C Kak.Digital Picture Processing[M].New York:Academic Press,1982.
[45]. Sondhi M M.Image Restoration:the removal of spatially invariant degrada-tions[J].Proceedings IEEE on Special Issue on Picture Processing,1972,60(7):842-853.
[46]. Hong Han-yu,Zhang Tian-XU.Investigation of restoration algorithm for d-egraded images caused by aerooptics efects using multi-resolution blind deconvolution[J].Chinese Journal of Computers,2004,27(7):952-963.
[47]. N Wiener.Extropolation,Interpolation and Smoothing of Stationary Time Series.MIT Press,Cambridge,MA,1942.
[48]. C W Helstrom.Image restoration by the method of least squares.Journal of the Optical Society of America,57:297-303,1967
[49]. D Slepian.Linear least-squares filtering of distorted images[J].Journal of the Optical Society of America.57:918-922,1967.
[50]. W K Pratt.Generalized Wiener filter computation techniques[J].IEEE Tran-sactions on Computer,21:636-641,1972.
[51]. Helstrom C W.Image restoration by the method of least squares[J].Jou-rnal of the Optical Society of America,1967,57(3):297-303.
[52]. Guan L,Ward R K.Restoration of randomly blurred images by the Wienerfilter[J].IEEE Transactions on Acoustics,Speech&Signal Processing,1989,37(7):589-592.
[53]. Spidit C A,Brodie I,Humphrey L,et al.Physical properties of thin-film fie-ld field-emission cathodes with molybdenum cones[J].J.Appl.Phys.,1976,47:5248.
[54]. 张红英,彭启琮.一种改进的模拟退火图像盲复原算法[J].电子科技大学学报.2006,35(5):767-769.
[55]. Eichmann G,Stojancic M.Superresolving signal and image restoration usin-g a linear associative memory[J].Appl.Opt,1987,vol.26:1911-1918.
[56]. Zhou Y.T,Chellappar,Jenkins B.K.Image restoration using a neural network[J].IEEE Trans.Acoust.Speech.Signal Processing,1988,36(7):1141-1151.
[57]. Paik J.K,Katsaggflos A.K.Image restoration using a modified Hopfield ne-twork[J].IEEE Trans.Image Processing.1992,1(1):49-63.
[58]. 韩玉兵,吴乐南.基于状态连续变化的 Hopfield 神经网络的图像复原[J].信号处理.2004,20(5):431-435.
[59]. 玄光男(日),程润伟著; 汪定伟,唐加福等译.遗传算法与工程设计 I-M].北京:科学出版社,2000.
[60]. Chen Y-W,Nakao Z, Xue F,et a1.A parallel genetic algorithm for image restoration[A].In:Proceedings of the 13th International Conference on P-attern Recognition[c], Vienna,Austria,1996,3:694-698.
[61]. 刘文耀等.光电图像处理[M].北京:电子工业出版社,2002.
[62]. Castleman K R.Digital Image Processing(数字图像处理).北京:清华大学出版社,1998,501-507.
[63]. 刘金根.一种基于现场定标的光电图像畸变校正算法[J].光子学报.2004,33(6),732-735.
[64]. Shah S,A ggarwa J K.a simple calibration procedure for fish-eye (high distortion) Lens Camera[J].Proceedings of IEEE International Conference onRobotics and Automation.1994:3422-3427.
[65]. 廖士中,高培焕,苏艺,王大鹏[J],中国图象图形学报.2000,5A(7),593-596.
[66]. Fraser S.Photogrammetric Camera Component Calibration:A Review of Analytical Techniques [C].Amlin Gruen,Thomas S Huang.Calibration and orientation of cameras in computer vision.Germany:Springer,2001:95-121.
[67]. Xu TianHua,Zhao JiZheng,Zhao YiGong.High-order-statistics based nonuniformity correction for infrared focal plane arrays [J].Journal of Xidian University,2006,33(2):227-230.(in Chinese)
[68]. Scribner D A,Sarkay K A,Caulfield J T,et al.Nonuniformity correction forstaring focal plane arrays using scene-based techniques.SPIE,1990,1308:224-233.
[69]. Scribner D A.Sarkay K A,Kruer M K,et al.Adaptive nonuniformity correction for IR focal plane arrays using neural networks.SPIE,1991,1541:100-109.
[70]. Zhang JinLi,Wang YuanQing,Zhu HongNa.A Simple Method of Accurate Aberration in a Camera with Large Field Lens[J].Journal of Optoelectro-nics?Laser,2006,17(2):158-161.(in Chinese)
[71]. Sun YuChen,Ge BaoZhen,Mu Bing,Sun Ming-rui,et al.Calibration of 3D Sensors with Linear Partition Method[J].Journal of Optoelectronics?Laser, 2005,16(2):135-139.(in Chinese)
[72]. Deng Y,Manjunath B S.Unsupervised segmentation of color—texture regions in images and video[J].IEEE Trans on PAMI,2001,23(8),800-810.
[73]. Carson C,Belongie S,Greenspan H,et a1.Biobworld:image segmentation us-ing expectation-maximization and its application to image query[J].IEEE Trans on PAMI,2002,24(8):1026-1038.
[74]. Jing F,Li M,Zhang H J,et a1.Unsupervised image segmentation using loc-al homogeneity analysis[J].Circuits and Systems,2003(2):456-459.
[75]. Deng Y N,Manjunath B S,Shin H.Color image segmentation[C].Proc IEEE Conf Computer Vision and Pattern Recognition.Fort Collins:[s.n.],1999:446-451.]
[76]. Staib L H,Duncan J S.Boundary finding with parametrically deformable models.IEEE Trans PAMI,1992,PAMI-14(11):1061-1075.
[77]. Haralick R M.Digital step edges from zero crossing of second directionalderivatives.IEEE Trans PAMI,1984,PAMI-6(1):58-68.
[78]. Nalwa V S,Binford T O,on deteching edges.IEEE Trans PAMI,1986,PAMI-8(6):699-714.
[79]. Canny J.a computational approach to edge detcetion[J].IEEE Trans PAMI,1986,PAMI-8(6):679-698.
[80]. Freeman W T,Adelson EH.the design and use of steerable filters[J].IEEE Trans PAMI,1991,PAMI-13(9):891-906.
[81]. Zhu S C,Mumford D.GRADE:Gibbs reaction and diffusion equations.In:P-roceedings of the International Conference on Computer Vision,1998:847-854.
[82]. Catte F,Lions P L,Morel J M,Coll T.Image selective smoothing and edge detection by nonlinear diffusion I[J],SIAM.J Numer Anal,1992,29(1):182-193.
[83]. Catte F,Lions P L,Morel J M,Coll T.Image selective smoothing and edge detection by nonlinear diffusion II[J],SIAM.J Numer Anal,1992,29(3):845-866.
[84]. Wilkinson M H F.Optimizing edge detectors for robust automatic thresho-ld selection:coping with edge curvature and noise[J].Graphic Model and Image Processing,1998,60(4):385-401.
[85]. Kass M,Witkin A,Terzopoulos D.Snakes:active contour models.InternationalJournal of Computer Vision,1988,1:321-331.
[86]. Levine M D,Leemet J.A method for non-purposive picture segmentation.In:Proceedings of the 3th International Joint Conference on Pattern Reco-gnition,1976.
[87]. Pong T C,Shapiro L G,Watson L T,Haralick R M.Experiments in segmen-tation using a facet model region grower[J].Computer Vision,Graphics,andImage Processing,1984,25(1):1-23.
[88]. 方德贤,刘国庆,董新宁,慕熙昱.区域生长法在多普勒天气雷达速度场分析中的应用[J].南京大学学报,2007,43(1):102-110.
[89]. 付郝,陈淑珍,吴娟娟,邹炼.区域生长法在扇形滤波器参数选择中的应用[J].武汉大学学报,2006,52(1):105-108.
[90]. 刘喜英,吴淑泉,徐向民.基于区域生长法的医学图像分割研究[J].重庆邮电学院学报,2004,16(2):49-51.
[91]. 刘文萍,吴立德.基于分形理论的一种图象分割方法[J].自动化学报,1998,24(4):557-561.
[92]. Zhang YongPing,zheng NanNing,Zhao RongChun.Variation-based approachto image segmentation[J].Science in China(Series F),2001,44(4):259-269.
[93]. A.S.Abutaleb.Automatic thresholding of gray-level pictures using two-dimensional entropies[J].Comput,Vision Graphics Image Process,1989,47(1):22-32.
[94]. Prasanna K.Sahoo,Gurdial Aror.a thresholding method based on two-dimensional Renyi’S entropy[J].Pattern Recognition,2004,37:1149-1161.
[95]. 刘良江,王耀南.灰度直方图和支持向量机在磁环外观检测中的应用[J].仪器仪表学报,2006,27(8):840-844.
[96]. Jiang LiHui,Guo YanYing.Image edge detection based on adaptive weighted morphology[J].Chinese Optics Letters,2007,5(2):77-78.
[97]. Hannah I,Patel D,Davies E R. the use of variance and entropic thresholding methods for image segmentation[J].Pattern Recognition,1995,28(8):1135-1144.
[98]. Sahoo P K,Wilkins C,Yeager J,Threshold selection using Rnyi's entropy[J].Pattern Rectgnition,1997,30(1):71-84.
[99]. Abutaled A S.Thresholding of gray-level pictures using two-dimensional entropy[J].Computer Vision,Graphics,and Image Processing,1989(1),47:22-32.
[100]. Geman S,Geman D.Stochastic relaxation,Gibbs distributions,and the Bayes-ian restoration of images[J].IEEE Trans PAMI,1984,PAMI-6(6):721-741.
[101]. Wang J P.Stochastic relaxation on partitions with connected components -and its application to image segmentation[J].IEEE Trans PAMI,1998,PA
[102]. Neri A,Colonnese S.Automatic moving object and background separation[J].Signal Precessing,1998,66:219-232.
[103]. 令狐永芳,郭小猛,王士同.基于 Markov 随机场模型的运动对象分割研究[J],计算机工程与应用,2007,43(14):56-59,86.
[104]. 赵银娣,张良培,李平湘.一种纹理特征融合分类算法[J].武汉大学学报,2006,31(3):278-281.
[105]. 仲兴荣,肖圣兵.基于高斯—马尔可夫随机场模型的运动目标自动分割[J].2003,19(3):78-83.
[106]. Ueda K, Extraction of signature and seal imprint from bankchecks by us-ing color information[J]. Proceedings of the Third International Conference on Document Analysis and Recognition, 1995, 2:665-672.
[107]. 艾武,彭磊,李玲玲,陈幼平等.用于支票印鉴鉴别的图像预处理及配准算法[J].计算机工程与应用.2003,29(3):34-37
[108]. 周琨,张大力. 印章鉴定系统的图像处理研究[J].计算机工程与应用.2002,38(20):218-220.
[109]. 廖闻剑,成瑜.一种基于笔划宽度的印鉴分割算法[J]. 数据采集与处理.2002,17(3):288-292
[110]. Cheng H.D.,Jiang X.H.,Wang J.Color image segmentation based on homo-gram thresholding and region merging[J].Pattern Recognition,2002,35(2):373-393.
[111]. ZhenYong Lin,Junxian Wang,Kai-Kuang Ma.Using eigencolor normalizatio-n for illumination-invariant color object recognition[J].Pattern Recognition,2002,35(11):2629-2670.
[112]. H D Cheng,X H Jiang,Y Sun,Jingli Wang.Color image segmentation:adva-nces and prospects[J].Pattern Recognition,2001,34(12):2259-2281.
[113]. Kan-Min Chen,Shu-Yuan Chen.Color texture segmentation using feature d-istributions[J],Pattern Recognition Letters,2002,23(7):755-771.
[114]. Shiuh-Ku Weng,Chung-Ming Kuo Wei-Cung Kang.Color texture segmenta-tion using color transform and feature distributions[J]. IEICE Transactionson information and Systems,2007:787-876.
[115]. Carlo Colomboa,Alberto Del Bimbo. Color-induced image representation and retrieval[J],Pattern Recognition,1999,32(10):1685-1695.
[116]. In Kyu Park,II Dong Yun,Sang Uk Lee. Color image retrieval using hybr-id graph representation[J],Image and Vision Computing,1999,17(7):465-474.
[117]. Aditya Vailaya,Anil Jain,Hong Jiang Zhang.On image classification:city images vs. land scapes[J],Patern Recognition,1998,31(12):1921-1935.
[118]. Fondon I,Serrano C,Acha B.Color-texture image segmentation based on multistep region growing[J].Optical Engineering,2006,45(5):57002-1-9.
[119]. H.0nishi,H.Suzuki. Detection of Rotation and Parallel Translation Using Hough and Fourier Transforms[J],IEEE Inter.Conf.Image Processing,1996,pp827-830.
[120]. E.D.Castro,C.Morandi. Registration of Translated and Rotated Images Using Finite Fourier Transforms[J],IEEE PAMI,1987,9(5).
[121]. P.V.C.Hough, Method and Means for recognizing Complex Pattterns, U.S.Patent,3069654,1962.
[122]. Hu X,Ahuja N.Matching point features with ordered geometric,rigidity an-d disparity constraints.IEEE Trans PAMI,1994,PAMI-16(10):1041-1049.
[123]. 桑农.图像匹配与分割方法研究[D].武汉:华中科技大学博士论文,2000
[124]. 王王慧燕. 图像边缘检测和图像匹配研究及应用[D].浙江:浙江大学博士学位论文,2003.
[125]. 张志佳.图像特征提取及目标匹配的理论和方法研究[J].沈阳:中国科学院沈阳自动化研究所博士学位论文,2005.
[126]. Zhao Yao,Yuan..Baozong.Skeleton extraction in seal identification system using morphology[J].International Conference on Signal Processing Proce-edings,ICSP,1998,2:1281-1283.
[127]. Marie-Pierre Dubuisson, Anil K. Jain,A Modified Hausdorff Distance for Object Matching[J],Proc. of 12th Int. Conf. on Pattern Recognition.Jerus-alem,Israel,1994:566-568.
[128]. Agarwal Pankaj K,Har-Peled, Sariel,et al.Hausdorff distance under translat-ion for points and balls[J].Proceedings of the Annual Symposium on Co-mputational Geometry,2003,282-291.
[129]. Huttenlocher D P,Rucklidge W J,Klanderman G A.Comparing images usi-ng the Hausdorff distance under translation[J].Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recogniti-on,1992,654-659..
[130]. Huttenlocher D P,Klanderman G A, William J R. Comparing images usi-ng the Hausdorff distance[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1993,15(9):850-863.
[131]. M P Dubuisson,A K Jain,2D matching of 3D moving objects in color outdoor scenes[J],In. Porc. IEEE Conf. CVPR,Seattle,WA,1994,887-894.
[132]. H Alt, B Behrends,J Blomer. measure the resemblance of polygonal shapes[J],Proc.Seventh Symposium on computation Geometry, 1991,1(1),253-258.
[133]. 于秋则. 合成孔径雷达(SAR)图像匹配导航技术研究[D].武汉:华中科技大学博士学位论文,2004.
[134]. Dong Gyu Sim, Oh Kyu Kwon, and Rae Hong Park.Object Matching A-lgorithms Using Robust Hausdorff Distance Measures[J], IEEE Transaction on image processing,1999,8(3): 425-429.
[135]. 汪亚明.图像匹配的鲁棒型 Hausdorff 方法[J].计算机辅助设计与图形学学报,2002,14(3): 238-241.
[136]. L G Robert.Machine perception of three-dimensional solids.In: Optical a-nd Electro-Optical Information Processing,Tippett J,et al.eds.,1965:159-197.
[137]. J M S Prewitt.Object enhancement and extraction in B. S. Lipkin and A.Rosenfeldeds.Picture Processing and Psychopictorics,1970.
[138]. Canny J.A computational approach to edge detection[J].IEEE Transactionson Pattern Analysis and Machine Intelligence,1986,PAMI-8(6):679-698.
[139]. D.Marr,E C Hildreth.Theory of edge detection[J].Proc.Royal Soc.of Londo-n, 1980,B207:187-217.
[140]. B Buxton.Early Image Processing Structural Techniques Motivated by Hu-man Visual Response.University of Surrey,1984.
[141]. H Freeman.On the encoding of arbitrary geometric configuration[J].IRE T-ransactions on Electronic Computers,1962,EC-10(2):260-268.
[142]. Sergios Theodoridis,Konstantinos Koutroumbas 著,李晶皎等译.模式识别[M].北京:电子工业出版社,2004.
[143]. 边肇祺,张学工.模式识别[M].北京:清华大学出版社,2005.
[144]. Silva F M,Almeida L B.Accelaration technique for the backprogagation algorithm[J].Proceedings of the EURASIP Workshop on Neural Networks(Al-meida L.B. et al.,eds.),1990,110-119.
[145]. 田军,虞厥邦,杨灿.适合于BP学习的共轭梯度法[J].电子科技大学学报,1994,23(5):462-467.
[146]. 周建华.共轭梯度法在 BP 网络中的应用[J].计算机工程与应用,1993(3):17-19.
[147]. 张颖,刘艳秋.软计算方法[M].北京:科学出版社,2002.
[148]. Hagan,Martin H.Training feedforward networks with the Marquardt algori-thm[J].IEEE Transactions on Neural Networks, 1994,5(6):989-993.
[149]. Ampazis N,Perantonis S J.Levenberg-Marquardt algorithm with adaptive momentum for the efficient training of feedforward networks[J].Proceedin-gs of the International Joint Conference on Neural Networks,2000,1:126-131.
[150]. Lendl M,Unbehauen R.Training neural networks with an extension of theMarquardt algorithm[J].Proceedings of NC 1998. International ICSC/IFAC Symposium on Neural Computation,1998,582-6.
[151]. Si J,Guian Zhou.A reduced memory Marquardt algorithm for training fee-dforward neural networks[J].Proceedings of the I3th World Congress International Federation of Automatic Control. Vol.I. Identification I,1997,233-6.
[152]. 阎平凡,张长水.人工神经网络与模拟进化计算[M].北京:清华大学出版社,2000.
[153]. 焦李成.神经网络计算[M].西安:西安电子科技大学出版社,1996.
[154]. 楼顺天,施阳.基于 MATLAB 的系统分析与设计一神经网络[M].西安:西安电子科技大学出版社,1998.
[155]. 汤天浩,刘以建,李杰.层次分类诊断模型的多重结构神经网络实现与应用[J].上海海运学院学报,1998,19(4):1-6.
[156]. 谭树彬.轧机厚控系统状态监测与故障诊断的研究与应用[D].沈阳:东北大学博士学位论文,2006.
[157]. Nielsen R H.Theory of the Back propagation neural network[J].Proc. of IJCNN, 1989,1:2593-2605.
[158]. 徐庐生.微机神经网络[M],北京:中国医药科技出版社,1995.
[159]. 高大启.有教师的线性基本函数前向三层神经网络结构研究[J].计算机学报,1998,21(1):80-85.
[160]. Texas Instruments. TMS320C6000 CPU and Instruction Set Reference G-uide[Z]. 2000.
[161]. 中华人民共和国公共安全行业标准-印章治安管理信息系统.http://www.safe-guard.com.cn/publication/org/sealsystem/seal1.html./2007-07-06.
[162]. 屈志毅,李炬嵘等.印鉴自动识别系统中图像处理与识别的方法研究[J].甘肃科学学报,2001,13(1):12-16.
[2]. 蔡国声. 印章三千年[M]. 上海:上海文化出版社, 1996.
[3]. 刘一闻. 印章[M].上海:上海古籍出版社, 1995.
[4]. 孙慰祖. 印章[M]. 上海:上海人民美术出版社, 1998.
[5]. 国务院关于国家行政机关和企业、事业单位印章的规定. http://bl.ningbo.gov.cn/news_read.php?id=9567&catg_code=news-01-05-02-18-01,2002-12-19/2007-4-8.
[6]. 中华人民共和国票据法. http://www.china.com.cn/chinese/law/647634.htm, 2004-8-9/2007-4-6.
[7]. 银行账户管理办法.http://www.gxu.edu.cn/administration/cwc/cwwj/zhgl/1114.htm,1994-10-09/2007-05-04.
[8]. 应严防伪造客户印鉴诈骗案.http://fol.math.sdu.edu.cn/jrfy/content/content.php?id=60&tb=dxal, 2002-05-11/2007-05-04.
[9]. 西安一妇女私刻“国家体育总局”印章诈骗 800 万. http://www.jxgdw.com/jxgd/news/shxw/userobject1ai598645.html, 2003-4-29/2007-04-08.
[10]. 北京三金融巨骗私刻首钢印章诈骗亿元被判极刑.http://china.eastday.com/epublish/gb/paper148/20030513/class014800003/hwz942073.htm,2003-05-13/2007-05-03.
[11]. 诈骗团伙伪造印鉴骗得 3500 万巨款.http://ent.caiku.com/pic/shwx/2006-5/31/2006053109485649.htm,2006-05-31/2007-07-20.
[12]. 揭密支票诈骗第一案.http://www.cctv.com/program/jjbxs/20050120/101298.shtml,2005-01-19/2007-07-20.
[13]. 印章防伪制作技术的发展原理及现状[J].中国防伪报道, 2005(1):3-4.
[14]. K Ueda, Y Nakamura. Automatic verification of seal impression pattern[J], Proc.7th Inter Conf Pattern Recognition(ICPR), 1984(2): 1019-1021.
[15]. Fan T J, Tsai W H. Automatic Chinese seal identification[J]. Computer Vision Graphics and Image Processing, 1984, 25(2): 311-330.
[16]. C M Chen. W H Tsai. Automatic identification of Chinese seal imprint with arbitrary or partial shapes[J]. Computer Processing Chinese OrientalLanguages, 1986,22(6): 216-233.
[17]. S Lee, J H Kim. Unconstrained seal imprint verification using attributed stroke graph matching[J]. Pattern Recognition, 1989, 22(6): 653-664.
[18]. Michael Yang-Min Fuh. Medial axis transformation and its application toseal imprint verification[D]. USA:Temple University, 2001.
[19]. Wen Gao, Sheng fu dong ,Xinlin Chen.A system for automatic Chinese seal imprint verification[J]. Proceedings of the Third International Confere-nce on Document Analysis and Recognition. 1995, pp. 660-664.
[20]. 陈燕新, 李卫东, 戚飞虎. 基于边缘和模板匹配的印鉴自动鉴别方法[J],上海交通大学学报,1998, 32(4): 44-48.
[21]. 杨新军, 王肇圻, 刘唯一, 母国光. 基于环投影模板匹配的印鉴鉴别法[J], 光电子.激光, 2001, 12(11): 1177-1181.
[22]. 耿建玲, 陈纯, 陈耀武, 黄轶伦.基于小波多尺度分解的印鉴图像配准[J], 计算机工程与应用,2006(1): 34-36.
[23]. Qing Hu, JingYu Yang, Qian Zhang, et al. An automatic seal imprint verification approach[J]. Pattern Recognition, 1995, 28(8):1251-1266.
[24]. Q. Hu, J. Yang, Q. Zhang and K. Liu, A seal-imprint verification system[J], Actaautomat. Sin. 1991, 17(6): 696-704.
[25]. J. Yang, Q. Hu, Q. Zhang and K. Liu, Automatic seal recognition for banking[J], Financial Comput. China 4, 1990: 14-16.
[26]. 胡庆, 杨静宇, 张黔, 等. 基于知识的印鉴鉴别方法[J]. 自动化学报,1991, 17(6): 696-704.
[27]. 张先萌, 罗安玉, 王翔, 等. 用矩不变量实现印鉴自动识别[J]. 电子学报, 1995, 23(4): 100-103.
[28]. M.K.Hu Visual pattern recognition by moment invariants[J]. IRE Trans. Inf. Theory, 1962: 179-187.
[29]. 电子验印方案. http://www.founderorder.com/product/productDetail.aspx?bh=42,/2007-05-08.
[30]. 引领验印新浪潮.http://www.hisuntech.com/aboutus/kanwu/2yingxiang03.htm,/2007-07-20.
[31]. 北大青鸟验印系统.http://www.jbbis.com.cn/solution/finance/diannaoyanyin.htm 2006.5.14/2007-05-14.
[32]. 金联安印章防伪鉴别系统.http://www.jla.com.cn/product1.htm,/2007-07-21.
[33]. Rafael C.Gonzalez,Richard E.Woods,阮秋琦,阮宇智等译.数字图像处理[M].北京:电子工业出版社.
[34]. 朱森良.计算机视觉[M].浙江:浙江大学出版社,1997.
[35]. 刘富强.数字视频监控系统开发及应用[M].北京:机械工业出版社,2003.
[36]. 章毓晋.图象分割[M].北京:科学出版社,2001.
[37]. 陈鲤江.基于彩色图像识别的目标跟踪研究[D].天津:南开大学博士生论文.2004.
[38]. 王庆有.CCD 应用技术[M].天津:天津大学出版社,2000.
[39]. 王庆有.图像传感器应用技术[M].北京:电子工业出版社,2003.
[40]. Blanksby A J,Loinaz M J.Performance Analysis of a Color CMOS Phot-ogate Image Sensor[J].IEEE Transactions,2000,47(1):55-64.
[41]. B.E.Bayer,Color Imaging Array,US Patent 3,971,065-1976.
[42]. M M Sondhi.Image restoration:the removal of spatially invariant degradat-ions[J].Proceedings IEEE,1967,60:842-853.
[43]. Andrews H C,Hunt B R.Digital image restoration[M].Englewood Clifs,New Jersey,USA:Prentice-Hall,1977.
[44]. A Rosenfeld,A C Kak.Digital Picture Processing[M].New York:Academic Press,1982.
[45]. Sondhi M M.Image Restoration:the removal of spatially invariant degrada-tions[J].Proceedings IEEE on Special Issue on Picture Processing,1972,60(7):842-853.
[46]. Hong Han-yu,Zhang Tian-XU.Investigation of restoration algorithm for d-egraded images caused by aerooptics efects using multi-resolution blind deconvolution[J].Chinese Journal of Computers,2004,27(7):952-963.
[47]. N Wiener.Extropolation,Interpolation and Smoothing of Stationary Time Series.MIT Press,Cambridge,MA,1942.
[48]. C W Helstrom.Image restoration by the method of least squares.Journal of the Optical Society of America,57:297-303,1967
[49]. D Slepian.Linear least-squares filtering of distorted images[J].Journal of the Optical Society of America.57:918-922,1967.
[50]. W K Pratt.Generalized Wiener filter computation techniques[J].IEEE Tran-sactions on Computer,21:636-641,1972.
[51]. Helstrom C W.Image restoration by the method of least squares[J].Jou-rnal of the Optical Society of America,1967,57(3):297-303.
[52]. Guan L,Ward R K.Restoration of randomly blurred images by the Wienerfilter[J].IEEE Transactions on Acoustics,Speech&Signal Processing,1989,37(7):589-592.
[53]. Spidit C A,Brodie I,Humphrey L,et al.Physical properties of thin-film fie-ld field-emission cathodes with molybdenum cones[J].J.Appl.Phys.,1976,47:5248.
[54]. 张红英,彭启琮.一种改进的模拟退火图像盲复原算法[J].电子科技大学学报.2006,35(5):767-769.
[55]. Eichmann G,Stojancic M.Superresolving signal and image restoration usin-g a linear associative memory[J].Appl.Opt,1987,vol.26:1911-1918.
[56]. Zhou Y.T,Chellappar,Jenkins B.K.Image restoration using a neural network[J].IEEE Trans.Acoust.Speech.Signal Processing,1988,36(7):1141-1151.
[57]. Paik J.K,Katsaggflos A.K.Image restoration using a modified Hopfield ne-twork[J].IEEE Trans.Image Processing.1992,1(1):49-63.
[58]. 韩玉兵,吴乐南.基于状态连续变化的 Hopfield 神经网络的图像复原[J].信号处理.2004,20(5):431-435.
[59]. 玄光男(日),程润伟著; 汪定伟,唐加福等译.遗传算法与工程设计 I-M].北京:科学出版社,2000.
[60]. Chen Y-W,Nakao Z, Xue F,et a1.A parallel genetic algorithm for image restoration[A].In:Proceedings of the 13th International Conference on P-attern Recognition[c], Vienna,Austria,1996,3:694-698.
[61]. 刘文耀等.光电图像处理[M].北京:电子工业出版社,2002.
[62]. Castleman K R.Digital Image Processing(数字图像处理).北京:清华大学出版社,1998,501-507.
[63]. 刘金根.一种基于现场定标的光电图像畸变校正算法[J].光子学报.2004,33(6),732-735.
[64]. Shah S,A ggarwa J K.a simple calibration procedure for fish-eye (high distortion) Lens Camera[J].Proceedings of IEEE International Conference onRobotics and Automation.1994:3422-3427.
[65]. 廖士中,高培焕,苏艺,王大鹏[J],中国图象图形学报.2000,5A(7),593-596.
[66]. Fraser S.Photogrammetric Camera Component Calibration:A Review of Analytical Techniques [C].Amlin Gruen,Thomas S Huang.Calibration and orientation of cameras in computer vision.Germany:Springer,2001:95-121.
[67]. Xu TianHua,Zhao JiZheng,Zhao YiGong.High-order-statistics based nonuniformity correction for infrared focal plane arrays [J].Journal of Xidian University,2006,33(2):227-230.(in Chinese)
[68]. Scribner D A,Sarkay K A,Caulfield J T,et al.Nonuniformity correction forstaring focal plane arrays using scene-based techniques.SPIE,1990,1308:224-233.
[69]. Scribner D A.Sarkay K A,Kruer M K,et al.Adaptive nonuniformity correction for IR focal plane arrays using neural networks.SPIE,1991,1541:100-109.
[70]. Zhang JinLi,Wang YuanQing,Zhu HongNa.A Simple Method of Accurate Aberration in a Camera with Large Field Lens[J].Journal of Optoelectro-nics?Laser,2006,17(2):158-161.(in Chinese)
[71]. Sun YuChen,Ge BaoZhen,Mu Bing,Sun Ming-rui,et al.Calibration of 3D Sensors with Linear Partition Method[J].Journal of Optoelectronics?Laser, 2005,16(2):135-139.(in Chinese)
[72]. Deng Y,Manjunath B S.Unsupervised segmentation of color—texture regions in images and video[J].IEEE Trans on PAMI,2001,23(8),800-810.
[73]. Carson C,Belongie S,Greenspan H,et a1.Biobworld:image segmentation us-ing expectation-maximization and its application to image query[J].IEEE Trans on PAMI,2002,24(8):1026-1038.
[74]. Jing F,Li M,Zhang H J,et a1.Unsupervised image segmentation using loc-al homogeneity analysis[J].Circuits and Systems,2003(2):456-459.
[75]. Deng Y N,Manjunath B S,Shin H.Color image segmentation[C].Proc IEEE Conf Computer Vision and Pattern Recognition.Fort Collins:[s.n.],1999:446-451.]
[76]. Staib L H,Duncan J S.Boundary finding with parametrically deformable models.IEEE Trans PAMI,1992,PAMI-14(11):1061-1075.
[77]. Haralick R M.Digital step edges from zero crossing of second directionalderivatives.IEEE Trans PAMI,1984,PAMI-6(1):58-68.
[78]. Nalwa V S,Binford T O,on deteching edges.IEEE Trans PAMI,1986,PAMI-8(6):699-714.
[79]. Canny J.a computational approach to edge detcetion[J].IEEE Trans PAMI,1986,PAMI-8(6):679-698.
[80]. Freeman W T,Adelson EH.the design and use of steerable filters[J].IEEE Trans PAMI,1991,PAMI-13(9):891-906.
[81]. Zhu S C,Mumford D.GRADE:Gibbs reaction and diffusion equations.In:P-roceedings of the International Conference on Computer Vision,1998:847-854.
[82]. Catte F,Lions P L,Morel J M,Coll T.Image selective smoothing and edge detection by nonlinear diffusion I[J],SIAM.J Numer Anal,1992,29(1):182-193.
[83]. Catte F,Lions P L,Morel J M,Coll T.Image selective smoothing and edge detection by nonlinear diffusion II[J],SIAM.J Numer Anal,1992,29(3):845-866.
[84]. Wilkinson M H F.Optimizing edge detectors for robust automatic thresho-ld selection:coping with edge curvature and noise[J].Graphic Model and Image Processing,1998,60(4):385-401.
[85]. Kass M,Witkin A,Terzopoulos D.Snakes:active contour models.InternationalJournal of Computer Vision,1988,1:321-331.
[86]. Levine M D,Leemet J.A method for non-purposive picture segmentation.In:Proceedings of the 3th International Joint Conference on Pattern Reco-gnition,1976.
[87]. Pong T C,Shapiro L G,Watson L T,Haralick R M.Experiments in segmen-tation using a facet model region grower[J].Computer Vision,Graphics,andImage Processing,1984,25(1):1-23.
[88]. 方德贤,刘国庆,董新宁,慕熙昱.区域生长法在多普勒天气雷达速度场分析中的应用[J].南京大学学报,2007,43(1):102-110.
[89]. 付郝,陈淑珍,吴娟娟,邹炼.区域生长法在扇形滤波器参数选择中的应用[J].武汉大学学报,2006,52(1):105-108.
[90]. 刘喜英,吴淑泉,徐向民.基于区域生长法的医学图像分割研究[J].重庆邮电学院学报,2004,16(2):49-51.
[91]. 刘文萍,吴立德.基于分形理论的一种图象分割方法[J].自动化学报,1998,24(4):557-561.
[92]. Zhang YongPing,zheng NanNing,Zhao RongChun.Variation-based approachto image segmentation[J].Science in China(Series F),2001,44(4):259-269.
[93]. A.S.Abutaleb.Automatic thresholding of gray-level pictures using two-dimensional entropies[J].Comput,Vision Graphics Image Process,1989,47(1):22-32.
[94]. Prasanna K.Sahoo,Gurdial Aror.a thresholding method based on two-dimensional Renyi’S entropy[J].Pattern Recognition,2004,37:1149-1161.
[95]. 刘良江,王耀南.灰度直方图和支持向量机在磁环外观检测中的应用[J].仪器仪表学报,2006,27(8):840-844.
[96]. Jiang LiHui,Guo YanYing.Image edge detection based on adaptive weighted morphology[J].Chinese Optics Letters,2007,5(2):77-78.
[97]. Hannah I,Patel D,Davies E R. the use of variance and entropic thresholding methods for image segmentation[J].Pattern Recognition,1995,28(8):1135-1144.
[98]. Sahoo P K,Wilkins C,Yeager J,Threshold selection using Rnyi's entropy[J].Pattern Rectgnition,1997,30(1):71-84.
[99]. Abutaled A S.Thresholding of gray-level pictures using two-dimensional entropy[J].Computer Vision,Graphics,and Image Processing,1989(1),47:22-32.
[100]. Geman S,Geman D.Stochastic relaxation,Gibbs distributions,and the Bayes-ian restoration of images[J].IEEE Trans PAMI,1984,PAMI-6(6):721-741.
[101]. Wang J P.Stochastic relaxation on partitions with connected components -and its application to image segmentation[J].IEEE Trans PAMI,1998,PA
[102]. Neri A,Colonnese S.Automatic moving object and background separation[J].Signal Precessing,1998,66:219-232.
[103]. 令狐永芳,郭小猛,王士同.基于 Markov 随机场模型的运动对象分割研究[J],计算机工程与应用,2007,43(14):56-59,86.
[104]. 赵银娣,张良培,李平湘.一种纹理特征融合分类算法[J].武汉大学学报,2006,31(3):278-281.
[105]. 仲兴荣,肖圣兵.基于高斯—马尔可夫随机场模型的运动目标自动分割[J].2003,19(3):78-83.
[106]. Ueda K, Extraction of signature and seal imprint from bankchecks by us-ing color information[J]. Proceedings of the Third International Conference on Document Analysis and Recognition, 1995, 2:665-672.
[107]. 艾武,彭磊,李玲玲,陈幼平等.用于支票印鉴鉴别的图像预处理及配准算法[J].计算机工程与应用.2003,29(3):34-37
[108]. 周琨,张大力. 印章鉴定系统的图像处理研究[J].计算机工程与应用.2002,38(20):218-220.
[109]. 廖闻剑,成瑜.一种基于笔划宽度的印鉴分割算法[J]. 数据采集与处理.2002,17(3):288-292
[110]. Cheng H.D.,Jiang X.H.,Wang J.Color image segmentation based on homo-gram thresholding and region merging[J].Pattern Recognition,2002,35(2):373-393.
[111]. ZhenYong Lin,Junxian Wang,Kai-Kuang Ma.Using eigencolor normalizatio-n for illumination-invariant color object recognition[J].Pattern Recognition,2002,35(11):2629-2670.
[112]. H D Cheng,X H Jiang,Y Sun,Jingli Wang.Color image segmentation:adva-nces and prospects[J].Pattern Recognition,2001,34(12):2259-2281.
[113]. Kan-Min Chen,Shu-Yuan Chen.Color texture segmentation using feature d-istributions[J],Pattern Recognition Letters,2002,23(7):755-771.
[114]. Shiuh-Ku Weng,Chung-Ming Kuo Wei-Cung Kang.Color texture segmenta-tion using color transform and feature distributions[J]. IEICE Transactionson information and Systems,2007:787-876.
[115]. Carlo Colomboa,Alberto Del Bimbo. Color-induced image representation and retrieval[J],Pattern Recognition,1999,32(10):1685-1695.
[116]. In Kyu Park,II Dong Yun,Sang Uk Lee. Color image retrieval using hybr-id graph representation[J],Image and Vision Computing,1999,17(7):465-474.
[117]. Aditya Vailaya,Anil Jain,Hong Jiang Zhang.On image classification:city images vs. land scapes[J],Patern Recognition,1998,31(12):1921-1935.
[118]. Fondon I,Serrano C,Acha B.Color-texture image segmentation based on multistep region growing[J].Optical Engineering,2006,45(5):57002-1-9.
[119]. H.0nishi,H.Suzuki. Detection of Rotation and Parallel Translation Using Hough and Fourier Transforms[J],IEEE Inter.Conf.Image Processing,1996,pp827-830.
[120]. E.D.Castro,C.Morandi. Registration of Translated and Rotated Images Using Finite Fourier Transforms[J],IEEE PAMI,1987,9(5).
[121]. P.V.C.Hough, Method and Means for recognizing Complex Pattterns, U.S.Patent,3069654,1962.
[122]. Hu X,Ahuja N.Matching point features with ordered geometric,rigidity an-d disparity constraints.IEEE Trans PAMI,1994,PAMI-16(10):1041-1049.
[123]. 桑农.图像匹配与分割方法研究[D].武汉:华中科技大学博士论文,2000
[124]. 王王慧燕. 图像边缘检测和图像匹配研究及应用[D].浙江:浙江大学博士学位论文,2003.
[125]. 张志佳.图像特征提取及目标匹配的理论和方法研究[J].沈阳:中国科学院沈阳自动化研究所博士学位论文,2005.
[126]. Zhao Yao,Yuan..Baozong.Skeleton extraction in seal identification system using morphology[J].International Conference on Signal Processing Proce-edings,ICSP,1998,2:1281-1283.
[127]. Marie-Pierre Dubuisson, Anil K. Jain,A Modified Hausdorff Distance for Object Matching[J],Proc. of 12th Int. Conf. on Pattern Recognition.Jerus-alem,Israel,1994:566-568.
[128]. Agarwal Pankaj K,Har-Peled, Sariel,et al.Hausdorff distance under translat-ion for points and balls[J].Proceedings of the Annual Symposium on Co-mputational Geometry,2003,282-291.
[129]. Huttenlocher D P,Rucklidge W J,Klanderman G A.Comparing images usi-ng the Hausdorff distance under translation[J].Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recogniti-on,1992,654-659..
[130]. Huttenlocher D P,Klanderman G A, William J R. Comparing images usi-ng the Hausdorff distance[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1993,15(9):850-863.
[131]. M P Dubuisson,A K Jain,2D matching of 3D moving objects in color outdoor scenes[J],In. Porc. IEEE Conf. CVPR,Seattle,WA,1994,887-894.
[132]. H Alt, B Behrends,J Blomer. measure the resemblance of polygonal shapes[J],Proc.Seventh Symposium on computation Geometry, 1991,1(1),253-258.
[133]. 于秋则. 合成孔径雷达(SAR)图像匹配导航技术研究[D].武汉:华中科技大学博士学位论文,2004.
[134]. Dong Gyu Sim, Oh Kyu Kwon, and Rae Hong Park.Object Matching A-lgorithms Using Robust Hausdorff Distance Measures[J], IEEE Transaction on image processing,1999,8(3): 425-429.
[135]. 汪亚明.图像匹配的鲁棒型 Hausdorff 方法[J].计算机辅助设计与图形学学报,2002,14(3): 238-241.
[136]. L G Robert.Machine perception of three-dimensional solids.In: Optical a-nd Electro-Optical Information Processing,Tippett J,et al.eds.,1965:159-197.
[137]. J M S Prewitt.Object enhancement and extraction in B. S. Lipkin and A.Rosenfeldeds.Picture Processing and Psychopictorics,1970.
[138]. Canny J.A computational approach to edge detection[J].IEEE Transactionson Pattern Analysis and Machine Intelligence,1986,PAMI-8(6):679-698.
[139]. D.Marr,E C Hildreth.Theory of edge detection[J].Proc.Royal Soc.of Londo-n, 1980,B207:187-217.
[140]. B Buxton.Early Image Processing Structural Techniques Motivated by Hu-man Visual Response.University of Surrey,1984.
[141]. H Freeman.On the encoding of arbitrary geometric configuration[J].IRE T-ransactions on Electronic Computers,1962,EC-10(2):260-268.
[142]. Sergios Theodoridis,Konstantinos Koutroumbas 著,李晶皎等译.模式识别[M].北京:电子工业出版社,2004.
[143]. 边肇祺,张学工.模式识别[M].北京:清华大学出版社,2005.
[144]. Silva F M,Almeida L B.Accelaration technique for the backprogagation algorithm[J].Proceedings of the EURASIP Workshop on Neural Networks(Al-meida L.B. et al.,eds.),1990,110-119.
[145]. 田军,虞厥邦,杨灿.适合于BP学习的共轭梯度法[J].电子科技大学学报,1994,23(5):462-467.
[146]. 周建华.共轭梯度法在 BP 网络中的应用[J].计算机工程与应用,1993(3):17-19.
[147]. 张颖,刘艳秋.软计算方法[M].北京:科学出版社,2002.
[148]. Hagan,Martin H.Training feedforward networks with the Marquardt algori-thm[J].IEEE Transactions on Neural Networks, 1994,5(6):989-993.
[149]. Ampazis N,Perantonis S J.Levenberg-Marquardt algorithm with adaptive momentum for the efficient training of feedforward networks[J].Proceedin-gs of the International Joint Conference on Neural Networks,2000,1:126-131.
[150]. Lendl M,Unbehauen R.Training neural networks with an extension of theMarquardt algorithm[J].Proceedings of NC 1998. International ICSC/IFAC Symposium on Neural Computation,1998,582-6.
[151]. Si J,Guian Zhou.A reduced memory Marquardt algorithm for training fee-dforward neural networks[J].Proceedings of the I3th World Congress International Federation of Automatic Control. Vol.I. Identification I,1997,233-6.
[152]. 阎平凡,张长水.人工神经网络与模拟进化计算[M].北京:清华大学出版社,2000.
[153]. 焦李成.神经网络计算[M].西安:西安电子科技大学出版社,1996.
[154]. 楼顺天,施阳.基于 MATLAB 的系统分析与设计一神经网络[M].西安:西安电子科技大学出版社,1998.
[155]. 汤天浩,刘以建,李杰.层次分类诊断模型的多重结构神经网络实现与应用[J].上海海运学院学报,1998,19(4):1-6.
[156]. 谭树彬.轧机厚控系统状态监测与故障诊断的研究与应用[D].沈阳:东北大学博士学位论文,2006.
[157]. Nielsen R H.Theory of the Back propagation neural network[J].Proc. of IJCNN, 1989,1:2593-2605.
[158]. 徐庐生.微机神经网络[M],北京:中国医药科技出版社,1995.
[159]. 高大启.有教师的线性基本函数前向三层神经网络结构研究[J].计算机学报,1998,21(1):80-85.
[160]. Texas Instruments. TMS320C6000 CPU and Instruction Set Reference G-uide[Z]. 2000.
[161]. 中华人民共和国公共安全行业标准-印章治安管理信息系统.http://www.safe-guard.com.cn/publication/org/sealsystem/seal1.html./2007-07-06.
[162]. 屈志毅,李炬嵘等.印鉴自动识别系统中图像处理与识别的方法研究[J].甘肃科学学报,2001,13(1):12-16.