纸币图像分析理解技术及其应用
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摘要
纸币图像分析理解技术对纸币流通监管具有十分重要的意义。本文对多光谱纸币图像进行深入分析,研究了快速纸币分类、纸币特征检测(污损特征、防伪特征)、纸币质量评估,设计完成了纸币图像分析系统,并应用到不同的实际产品中。
在快速纸币分类中,采用分步识别策略。首先采用“网格-GMM”方法进行预分类,然后采用“类Haar-AdaBoost”分类器区分相似纸币。在预分类过程中,提取网格特征,然后采用基于结构风险最小化(SRM)的混合高斯模型(GMM)构造分类器,以提高识别的稳定性。在相似纸币区分中,首先提出了类Haar特征的提取方法,然后采用AdaBoost算法选取有效特征,以提高分类器的区分能力。实验证明,该分步策略显著提高了系统对于低质量纸币的处理能力,大幅降低了系统拒识率。
在纸币特征检测中,采用基于图像配准/匹配的检测算法。在第一种基于边缘特征的算法中,首先采用基于图像区域的配准算法,以确定待检图像与参考图像之间的对应关系。然后采用Kirsch算子计算两图像的边缘强度差(EID),并根据EID来提取缺损特征。该算法对于图像上的新增边缘十分敏感,而对整体亮度偏移则较为稳定,在处理折旧纸币时,拥有较好的鲁棒性。在第二种算法中,提出首先采用均匀性特征确定污损待检象素,然后采用基于均匀性特征的图像配准算法确定待检图像与参考图像间的象素对应关系,最后将污损待检象素与其参考象素进行比较,以确定缺损状况并进行质量分析。在第三种方法中,将均匀性特征推广到彩色空间,取得了更为精确的质量分析结果。
在纸币退化分析中,首次详细讨论了纸币的自身特点,并据此对纸币退化及形变(D&D)模型进行了研究。将纸币形变采用基于B-样条的FFD网格来表示,将纸币退化采用颜色混合模型(CDM)来表示。在CDM中,纸币退化被看作“全局磨损”与“局部缺损”共同作用的结果,并据此将参考图像到待检图像的颜色偏移分解为磨损折旧及缺损位移,分别进行评估。在第一种算法中,构建纸币退化能量函数(BDE)作为数据驱动项,进行基于FFD模型的图像配准,实现对纸币退化的量化分析。第二种算法中,对于每一类纸币,将原有的静态参考图像扩展为动态的退化图像序列,将纸币质量评估看作在相应序列中寻找对应帧的过程。利用3维FFD模型,同时考虑纸币的退化及形变,显著地提高了纸币分析的整体性能。
在纸币分析系统构建中,首先介绍了高速多光谱接触式图像传感器(CIS)的设计原理,通过该传感器可以依次采集纸币的红、绿以及红外光谱图像,然后提出了“FPGA+DSP+上位机”的纸币分析系统构架。在硬件配置中,讨论了DSP的外设配置以及FPGA的逻辑设计,包括触发控制,曝光控制,行采集以及I/O端口设计等。在软件设计中,首先讨论了图像采集流程,然后提出了传感器补偿算法,用于补偿CIS各采集单元的成像误差,最后讨论了图像分析流程。在图像分析流程中,在DSP片内实现实时性的基本功能,如纸币分类,缺损/防伪特征检测以及质量评估,将其他的扩展功能留在上位机实现。采用该构架设计了两款纸币分析系统,第一款用于CF3000型纸币清分机,该系统中,通过数个图像分析模块的组合,实现对纸币的全面分析。第二款用于WJD_TKTH07型纸币点钞检伪仪,该系统中,将数项功能集成于同一处理模块。实验证明,该构架设计灵活,可适用于各种不同的应用场合。
Banknote image analysis is of vital importance to the currency supervision. In this thesis, the muti-spectrum images of banknote are systematically investigated for the first time. The algorithms of banknote image analysis are first studied including banknote classification, feature detection/verification (defect feature or anti-counterfeit feature), and quality evaluation, based on which, the system of banknote image processing is then achieved and applied to different enverinments.
In banknote classification, a hierarchical recognition strategy is proposed. First, the banknotes are pre-classified by the Grid-GMM Method, and then the Haar-Adaboost classifiers are used for the identification between the similar banknotes. During the pre-calssification, Grid Features are adopted, and a Gaussian mixture model (GMM) based on structural risk minimization (SRM) is engaged to build a more robust classifer. During the similar banknote identification, a kind of Haar-like feature is proposed, which is then refined by the Adaboost method to gain high performance on the classification of the similar images. The exeperimental result reveals that this hierachical method leads to a high capacity for low quality banknote processing and greatly decreases the false reject rate.
In banknote feature detection/verification, three algorithms based on image registration/match are proposed. In the first edge-based algorithm, an area-based image registration algorithm is adopted to overlay the sensed and reference banknote images, in which an Edge Intensity Differential (EID) of the two images is constructed from the edge information extracted by the Kirsch operator. The Defect Feature extracted by EID is sensitive to the odd edge-information, and is robust to the global intensity change, which makes it suit for the attrited banknote. In the second algorithm, the homogeneity feature of banknote are introduced to locate the pixels that probably been blurred, the image registration algorithm based on the homogeneity feature is subsequently used to overlay the sensed and reference paper currency image. At last, each probably polluted pixel on the sensed image is compared with its corresponding pixel on the reference image to estimate the deterioration level. In the third algorithm, the homogeneity feature is extended to the chromatic space, which gains a more accurate result on banknote quality evaluation.
In banknote deterioroation analysis, the characteristics of banknotes are discussed in detail for the first time. Based on it, a deformation and deterioration (D&D) model of banknote are proposed, in which the banknote deformation is interpreted by a cubic B-spline based Free Form Deformation (FFD) grids and the banknote deterioration is described by a Color Diffusion Model(CDM). In the CDM, banknote deterioration is regarded as a joint effect of General Attrition and Local Defect, and accordingly, the color shift from the sensed image to the reference image can be decomposed into the Attrition Rate and Defect Distance and evaluated separately. Based on the D&D model, two banknote evaluation algorithms are proposed. In the first one, a Banknote Deterioration Energy (BDE) is proposed as the data-driven term in FFD-based image registration, by which the quantitative analysis of banknote deterioration achieved. In the second one, for each kind of banknote image, the static reference image is extended to a dynamic image sequence that changed according to the deterioration degree. Then, the quality evaluation of the sensed image can be interpreted as locating its optimal position in the corresponding sequence. In this three-dimension FFD model, the deterioration degree and deformation state of one banknote can be concerned simultaneity, which will improve the banknote analysis performance greatly.
In the banknote analysis system, a high speed Muti-spectrum Contact Image Sensor (CIS) is first introduced, by which one banknote can be alternately sampled under the red, green, and infrared lights, and then a“FPGA+DSP+PC”framework for the banknote analysis system is proposed. In the hardware configuration, the peripherals of the DSP and the logic setting of the FPGA are discussed, including trigger control, exposure contral, line sampling, and the I/O ports setting. In the software design, the image sampling flow are first discussed, and then a sensor compensation algorithm is proposed to equalize the sensitive error of each sampling unit in CIS, and at last, the image analysis scheme is discussed. In the image analysis scheme, the real-time basic function is implemented inside the DSP, such as banknote classification, defect/anti- counterfeit feature detection and quality evaluation, and the other extended function can be implemented in PC. Using this framework, two banknote analysis systems are constructed. The first one is designed for the CF3000 banknote sorter, in which several image analysis modules are associated to achieve a full analysis of the banknotes. The second one is designed for WJD_TKTH07 anti-counterfeit banknote counter, in which several functions are integrated into one image analysis module. The experinmental result reveals that this framework is flexible enough to be applied to different conditions.
在快速纸币分类中,采用分步识别策略。首先采用“网格-GMM”方法进行预分类,然后采用“类Haar-AdaBoost”分类器区分相似纸币。在预分类过程中,提取网格特征,然后采用基于结构风险最小化(SRM)的混合高斯模型(GMM)构造分类器,以提高识别的稳定性。在相似纸币区分中,首先提出了类Haar特征的提取方法,然后采用AdaBoost算法选取有效特征,以提高分类器的区分能力。实验证明,该分步策略显著提高了系统对于低质量纸币的处理能力,大幅降低了系统拒识率。
在纸币特征检测中,采用基于图像配准/匹配的检测算法。在第一种基于边缘特征的算法中,首先采用基于图像区域的配准算法,以确定待检图像与参考图像之间的对应关系。然后采用Kirsch算子计算两图像的边缘强度差(EID),并根据EID来提取缺损特征。该算法对于图像上的新增边缘十分敏感,而对整体亮度偏移则较为稳定,在处理折旧纸币时,拥有较好的鲁棒性。在第二种算法中,提出首先采用均匀性特征确定污损待检象素,然后采用基于均匀性特征的图像配准算法确定待检图像与参考图像间的象素对应关系,最后将污损待检象素与其参考象素进行比较,以确定缺损状况并进行质量分析。在第三种方法中,将均匀性特征推广到彩色空间,取得了更为精确的质量分析结果。
在纸币退化分析中,首次详细讨论了纸币的自身特点,并据此对纸币退化及形变(D&D)模型进行了研究。将纸币形变采用基于B-样条的FFD网格来表示,将纸币退化采用颜色混合模型(CDM)来表示。在CDM中,纸币退化被看作“全局磨损”与“局部缺损”共同作用的结果,并据此将参考图像到待检图像的颜色偏移分解为磨损折旧及缺损位移,分别进行评估。在第一种算法中,构建纸币退化能量函数(BDE)作为数据驱动项,进行基于FFD模型的图像配准,实现对纸币退化的量化分析。第二种算法中,对于每一类纸币,将原有的静态参考图像扩展为动态的退化图像序列,将纸币质量评估看作在相应序列中寻找对应帧的过程。利用3维FFD模型,同时考虑纸币的退化及形变,显著地提高了纸币分析的整体性能。
在纸币分析系统构建中,首先介绍了高速多光谱接触式图像传感器(CIS)的设计原理,通过该传感器可以依次采集纸币的红、绿以及红外光谱图像,然后提出了“FPGA+DSP+上位机”的纸币分析系统构架。在硬件配置中,讨论了DSP的外设配置以及FPGA的逻辑设计,包括触发控制,曝光控制,行采集以及I/O端口设计等。在软件设计中,首先讨论了图像采集流程,然后提出了传感器补偿算法,用于补偿CIS各采集单元的成像误差,最后讨论了图像分析流程。在图像分析流程中,在DSP片内实现实时性的基本功能,如纸币分类,缺损/防伪特征检测以及质量评估,将其他的扩展功能留在上位机实现。采用该构架设计了两款纸币分析系统,第一款用于CF3000型纸币清分机,该系统中,通过数个图像分析模块的组合,实现对纸币的全面分析。第二款用于WJD_TKTH07型纸币点钞检伪仪,该系统中,将数项功能集成于同一处理模块。实验证明,该构架设计灵活,可适用于各种不同的应用场合。
Banknote image analysis is of vital importance to the currency supervision. In this thesis, the muti-spectrum images of banknote are systematically investigated for the first time. The algorithms of banknote image analysis are first studied including banknote classification, feature detection/verification (defect feature or anti-counterfeit feature), and quality evaluation, based on which, the system of banknote image processing is then achieved and applied to different enverinments.
In banknote classification, a hierarchical recognition strategy is proposed. First, the banknotes are pre-classified by the Grid-GMM Method, and then the Haar-Adaboost classifiers are used for the identification between the similar banknotes. During the pre-calssification, Grid Features are adopted, and a Gaussian mixture model (GMM) based on structural risk minimization (SRM) is engaged to build a more robust classifer. During the similar banknote identification, a kind of Haar-like feature is proposed, which is then refined by the Adaboost method to gain high performance on the classification of the similar images. The exeperimental result reveals that this hierachical method leads to a high capacity for low quality banknote processing and greatly decreases the false reject rate.
In banknote feature detection/verification, three algorithms based on image registration/match are proposed. In the first edge-based algorithm, an area-based image registration algorithm is adopted to overlay the sensed and reference banknote images, in which an Edge Intensity Differential (EID) of the two images is constructed from the edge information extracted by the Kirsch operator. The Defect Feature extracted by EID is sensitive to the odd edge-information, and is robust to the global intensity change, which makes it suit for the attrited banknote. In the second algorithm, the homogeneity feature of banknote are introduced to locate the pixels that probably been blurred, the image registration algorithm based on the homogeneity feature is subsequently used to overlay the sensed and reference paper currency image. At last, each probably polluted pixel on the sensed image is compared with its corresponding pixel on the reference image to estimate the deterioration level. In the third algorithm, the homogeneity feature is extended to the chromatic space, which gains a more accurate result on banknote quality evaluation.
In banknote deterioroation analysis, the characteristics of banknotes are discussed in detail for the first time. Based on it, a deformation and deterioration (D&D) model of banknote are proposed, in which the banknote deformation is interpreted by a cubic B-spline based Free Form Deformation (FFD) grids and the banknote deterioration is described by a Color Diffusion Model(CDM). In the CDM, banknote deterioration is regarded as a joint effect of General Attrition and Local Defect, and accordingly, the color shift from the sensed image to the reference image can be decomposed into the Attrition Rate and Defect Distance and evaluated separately. Based on the D&D model, two banknote evaluation algorithms are proposed. In the first one, a Banknote Deterioration Energy (BDE) is proposed as the data-driven term in FFD-based image registration, by which the quantitative analysis of banknote deterioration achieved. In the second one, for each kind of banknote image, the static reference image is extended to a dynamic image sequence that changed according to the deterioration degree. Then, the quality evaluation of the sensed image can be interpreted as locating its optimal position in the corresponding sequence. In this three-dimension FFD model, the deterioration degree and deformation state of one banknote can be concerned simultaneity, which will improve the banknote analysis performance greatly.
In the banknote analysis system, a high speed Muti-spectrum Contact Image Sensor (CIS) is first introduced, by which one banknote can be alternately sampled under the red, green, and infrared lights, and then a“FPGA+DSP+PC”framework for the banknote analysis system is proposed. In the hardware configuration, the peripherals of the DSP and the logic setting of the FPGA are discussed, including trigger control, exposure contral, line sampling, and the I/O ports setting. In the software design, the image sampling flow are first discussed, and then a sensor compensation algorithm is proposed to equalize the sensitive error of each sampling unit in CIS, and at last, the image analysis scheme is discussed. In the image analysis scheme, the real-time basic function is implemented inside the DSP, such as banknote classification, defect/anti- counterfeit feature detection and quality evaluation, and the other extended function can be implemented in PC. Using this framework, two banknote analysis systems are constructed. The first one is designed for the CF3000 banknote sorter, in which several image analysis modules are associated to achieve a full analysis of the banknotes. The second one is designed for WJD_TKTH07 anti-counterfeit banknote counter, in which several functions are integrated into one image analysis module. The experinmental result reveals that this framework is flexible enough to be applied to different conditions.
引文
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50 Jong Pil Yun; YoungSu Park; Boyeul Seo; Sang Woo Kim; Se Ho Choi; Chang Hyun Park; Ho Mun Bae; Hwa Won Hwang. Development of Real-time Defect Detection Algorithm for High-speed Steel Bar in Coil(BIC). SICE-ICASE. 2006:2495-2498
51 Kumar, A. Computer-Vision-Based Fabric Defect Detection: A Survey., IEEE Transactions on Industrial Electronics. 2008, 55(1):348-363
52 Hiroyuki Onishi,Shoji Tatsumi. A Pattern Defeat Inspection Method by Parallel Grayscale Image Comparison without Precise Image Alignment. IEEE 2002
53 V. Scotti, F. Roveri. Visual inspection of particle boards for quality assessment Piuri. IEEE International Conference on Image Processing, 2005, 3: 521-525
54 Michael S. Brown and W. Brent Seales. Image Restoration of Arbitrarily Warped Documents. IEEE Trans. on PAMI, Vol. 26, No. 10, 2004:1295-1306
55 Vittoria Bruni and Domenico Vitulano. A Generalized Model for Scratch Detection. IEEE Trans. Image Processing., Vol. 13, No. 1, 2004:44-50
56 Tsui, B.M.W., Lalush, D.S., Frey, E.C., Gullberg, G.T.. Evaluation of myocardial defect detection between parallel-hole and fan-beam SPECT using the Hotelling trace Wollenweber. IEEE Transactions on Nuclear Science. 1998, 45(4):2205-2210
57 Wollenweber. S.D.,Tsui, B.M.W.,Lalush, D.S.,Frey, E.C.,LaCroix, K.J., Gullberg, G.T.. Comparison of Hotelling observer models and human observers in defect detection from myocardial SPECT imaging. IEEE Transactions on Nuclear Science. 1999, 46(6):2098-2103
58 Mircea Ionescu and Anca Ralescu. Fuzzy Hamming Distance BasedBanknote Validator. IEEE International Conference on Fuzzy Systems. 2005:300-305
59 C. He, M. Girolami, and G. Ross. Employing optimized combinations of one-class classifiers for automated currency validation. Pattern Recognition 2004, 37(6):1085–1096.
60 Masaru Teranishi, T. Matsui, Sigeru Omatu and Toshihisa Kosaka. Neuro-classification of fatigued bill based on tensional acoustic signal. SMCIA 2005: 173-177
61 Teranishi Omatu and Kosaka. New and used bills classification for cepstrum patterns. IJCNN, 1999: 3978-3981
62 Teranishi Omatu and Kosaka. Classification of bill fatigued levels by feature-selected acoustic energy pattern using competitive neural network. IJCNN, 2001: 249-253
63 Teranishi Omatu and Kosaka. Neuro-classification of bill fatigue levels based on acoustic wavelet components. ICANN ,2002: 237-241
64梁友杰,人民币防伪技术及真伪鉴别,中国金融出版社, 2005.
65陈伟,赵艳华.银行纸币鉴伪系统设计.自动化技术与应用.2003,22(3):53-56
66张力,黄建同.关于第五套人民币2005年版纸币防伪特征的研究.警察技术, 2006, 6:50-52
67唐春晖,沈伟良.全自动人民币伪币鉴别仪的设计.仪表技术,2006, 3: 23-25
68党选举,原军胜.纸质传感器在点伪钞机中的应用.传感器技术, 1995, 1:55-58
69 Hiroshi Sako, Takashi Watanabe, Hiroto Nagayoshi, and Tatsuhiko Kagehiro. Self-Defense-Technologies for Automated Teller Machines. IEEE International Machine Vision and Image Processing Conference, 2007: 177-184.
70何佳兵,李习伦,刘松波,王希奎.纸币清分机系统的研究与开发,机电工程技术,2007,36(8): 29-32.
71 Daishi Suzuki. Banknote validating apparatus and method. U.S. Patent, Patent number: 7084416
72 Otsuka. bank note processing apparatus and bank note processing methodU.S. Patent, Patent number: 6659260
73 TienYuan Chien, Banknote receiver for ticket vendor , U.S. Patent, Patent number: 6983881
74 Recycling of Euro Banknotes: Framework for the detection of counterfeits and fitness sorting by credit institutions and other professional cash handlers. European central bank. Jan.2005
75 U.V.Chaudhari, J.Navratil. Multigrained Modeling with Pattern Specific Maximum Likelihood Transformations for Text-Independent Speaker Recognition. IEEE Transactions on Speech and Audio Processing. 2003, 11(1): 478-485
76 A. Hedelin , J. Skoglund. Vector Quantization Based on Gaussian Mixture Models. IEEE Trans. Speech and Audio Processing, 2000, 8 (4): 385-401
77 Martin H.C. Law, Mário A.T. Figueiredo and Anil K. Jain. Simultaneous Feature Selection and Clustering Using Mixture Models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2004, 26(9): 1154-1166
78 Mario A.T. Figueiredo, Anil K.Jain. Unsupervised Learning of Finite Mixture Models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002, 24(3):381-396
79 Constantinos Constantinopoulos. Michalis K. Titsias, and Aristidis Likas. Bayesian Feature and Model Selection for Gaussian Mixture Models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2006, 28(6): 1013-1018
80 Vapnik V. Estimation of Dependencies Based on Empirical Data. Springer, 1982
81 Vapnik V. Statistical Learning Theory. John Wiley, 1998
82 Ben Hamza, Hamid Krim, B. Karacali. Structural Risk Minimization Using Nearest Neighbor Rule. ICASSP, 2003: 161-165
83 John Shawe-Taylor, Peter L. Bartlett. Structural Risk Minimization Over Data-Dependent Hierarchies. IEEE Transactions on Information Theory. 1998, 44(5):347-358
84 Andrew Webb. Statistical Pattern Recognition. Oxford University Press, New York, 1999.
85 C. Papageorgiou, M. Oren, and T. Poggio. A general framework for objectdetection. International Conference on Computer Vision, 1998:2173-2178
86 A D. Roth, M. Yang, and N. Ahuja. A snowbased face detector. Neural Information Processing, 2000:490-495
87 Yoav Freund and Robert E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Computational Learning Theory, Eurocolt’95. Springer-Verlag, 1995:23–37.
88 S.Mori, C.Y.Suen and K.Yamamoto. Historical Review of OCR Research and Development. Proceedings of the IEEE. 1992, 80:1029-1058
89 R.Plamondon. On-line and Off-line Handwriting Recognition: a Comprehensive Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000, 22(1): 63-84
90 S.V. Rice, G. Nagy and T.A. Nartker. Optical Character Recognition: An illustrated guide to the frontier. Kluwer Academic Publishers, 1999
91 R. C. Gonzalez and P.Wintz, Digital Image Processing. Reading, MA: Addison -Wesley, 1987.
92 HengDa Cheng, Ying Sun. A hierarchical approach to color image segmentation using homogeneity, IEEE Trans on Image Processing, 2000, 9(12):2071~2082
93 Hanzi Wang, David Suter. Color Image Segmentation Using Global Information and Local Homogeneity. Proc. 7th Digital Image Computing: Techniques and Applications. 2003:89-98
94色彩学编写组,色彩学.科学出版社, 2001
95 R.W. G. Hunt.Measuring Color.2nd ed. Chichester. U.K. Ellis Horwood, 1987.
96 Luca Lucchese, Sanjit K. Mitra. A new class of chromatic filters for color image processing:theory and applications. IEEE Trans on Image Processing, 2004, 13(4): 534-548
97 F. L. Bookstein. Principal warps: Thin-plate splines and the decomposition of deformations. IEEE Trans. Pattern Anallsis Machine Intelligence. 1989, 11(6): 567-585.
98 M. H. Davis, A. Khotanzad, D. P. Flamig, and S. E. Harms. A physicsbased coordinate transformation for 3-D image matching. IEEE Trans. Medical Image. 1997, 16(5): 317–328.
99 S. Lee, G. Wolberg, K.-Y. Chwa, and S. Y. Shin. Image metamorphosis with scattered feature constraints. IEEE Trans. Visualization Comput. Graph. 1996,2(11) 337–354
100 S. Lee, G. Wolberg, and S. Y. Shin. Scattered data interpolation with multilevel B-splines. IEEE Trans. Visualization Comput. Graph.. 1997, 3(7): 228–244
101 E. Bardinet, L. D. Cohen, and N. Ayache,“Tracking and motion analysis of the left ventricle with deformable superquadrics,”Med. Image Anal.. 1996 1(2):129–149
102 Julia A. Schnabel, Christine Tanner, Andy D. Castellano-Smith, Andreas Degenhard, Martin O. Leach, D. Rodney Hose, Derek L. G. Hill, and David J. Hawkes. Validation of Nonrigid Image Registration Using Finite-Element Methods: Application to Breast MR Images. IEEE Transactions on medical imaging. 2003, 22(2): 238-247
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5 F Takeda, S Omatu. A neuro-recognition technology for paper currency using optimized masks by CA and its hardware, Proceedings of the International Conference on Information Systems Analysis and Synthesis, Orlando, USA,1996:147-152
6 F Takeda, S Omatu. and Nishikage. Neural network recognition system tuned by CA and design of its Hardware by DSP, Proceedings of International Symposium on Artificial Intelligence in Real-Time Control, Malaysia, 1997: 356-362
7 F Takeda, S Omatu,“Neural Network Systems Technique and Applications in Paper Currency Recognition”, Neural Network Systems, Techniques and Applications, 1998, 5: 133-160
8 F.Takeda, T. Nishikage, and Y. Matsumoto,“Characteristic Extraction of Paper Currency using Symmetrical Masks Optimized by GA and Neuro-Recognition of Multi-National Paper Currency”, Proceedings of IEEE World Congress on Computational Intelligence, Alaska, USA, 1998,1: 634-639
9 Nishikage, T. and Takeda, E:“Axis-Symmetrical Masks Optimized by CA for Neuro-Currency Recognition and their Statistical Analysis”, Proceedings of World Multi-Conference on Systemics, Cybernetics and Informatics,Orlando, USA, 1998:308-314
10 Takeda, E, Omatu, S. and Matsumoto, Y.:“Development of High Speed Neuro-Recognition Board and Application for Paper Currency”, The international Workshop on Signal Processing Application and Technology, 1998, 7:49-56
11 A. Frosini, M. Gori, and P. Priami,“A neural network based model for paper currency recognition and verification,”IEEE Trans. Neural Networks, 1996, 7: 1482～1490.
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14 A. Ahmadi and S. Omatu,“A High Reliabilily Method for Classification of Paper Currency Based on Neural Networks,”Proceeding of The Eighth International Symposium an Artificial Life and Robotics, Oita, Japan, 2003, 2:601-604.
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18李国华.基于TMS320F2812的小型纸币鉴伪/清分机.电子技术.2004, 32(08): 355-358
19何卫国,曹益平,梁友生,赵新亮,常山.基于DSP实现的光电点钞系统数据处理方法研究.光学技术. 2004, 28(04): 312-317
20 F. Kong, J. Ma and J. Liu,“Paper Currency Recognition using Gaussian Mixture Models Based on Structural Risk Minimization,”Proceedings of the Fifth Int. Conf. on Machine Learning and Cybernetics, 2006:3213-3217
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22孔繁辉,高速清分机人民币面值面向识别技术的研究,哈尔滨工业大学硕士学位论文, 2004.
23 P. Viola and M. Jones, Rapid Object Detection using a Boosted Cascade of Simple Features, IEEE International conference on computer vision and pattern recognition, Hawaii, 2001: 511-518
24 P. Viola M. Jones and D. Snow, Detecting Pedestrians Using Patterns of Motion and Appearance Proceedings of the International Conference on Computer Vision, Nice, France. 2003: 1324-1331
25王立威王作英基于结构风险最小化准则的高斯混合模型的参数估计声学学报, 2003, 28(5): 465-469
26张国华,梁中华,一种基于模板匹配的人民币纸币面额识别方法,沈阳工业大学学报,2005, 27(4): 439-442
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30 Z. Zhang,“Iterative Point Matching for Registration of Free-Form Curves and Surfaces,”Int’l J. Computer Vision, 1994, 13(2): 119-152
31 P.J. Besl and N.D. McKay,“A Method for Registration of 3-D Shapes,”IEEE Trans. Pattern Analysis and Machine Intelligence, 1992, 14(2): 239-256
32 S. Belongie, J. Malik, and J. Puzicha,“Matching Shapes,”Proc. IEEE Int’l Conf. Computer Vision, 2001: 456-461
33 T. Cootes, C. Taylor, D. Cooper, and J. Graham. Active Shape Models—Their Training and Application, Computer Vision and Image Understanding, 1995, 61: 38-59
34 R.H. Davies, C.J. Twining, T.F. Cootes, J.C. Waterton, and C.J. Taylor. 3D Statistical Shape Models Using Direct Optimization of Description Length. Proc. European Conf. Computer Vision. 2002: 3-20
35 T. Sebastian, P. Klein, and B. Kimia. Alignment-Based Recognition of Shape Outlines. Lecture Notes in Computer Science. 2001, 20: 606-618.
36 H. Chui and A. Rangarajan. A New Algorithm for Non-Rigid Point Matching. IEEE Conf. Computer Vision and Pattern Recognition. 2000: 44-51
37 D. Metaxas. Physics-Based Deformable Models. Kluwer Academic, 1996
38 L.H. Staib and J.S. Duncan. Boundary Finding with Parametrically Deformable Models. IEEE Trans. Pattern Analysis and Machine Intelligence, 1992, 14(11):1061-1075
39 T. Sebastian, P. Klein, and B. Kimia. Recognition of Shapes by Editting Shock Graphs. Proc. IEEE Int’l Conf. Computer Vision. 2001: 755-762
40 M. Leventon, E. Grimson, and O. Faugeras. Statistical Shape Influence in Geodesic Active Contours. Proc. IEEE Conf. Computer Vision and Pattern Recognition. 2001, I: 316-322
41 C. Chefd’Hotel, G. Hermosillo, and O. Faugeras. A Variational Approach to Multi-Modal Image Matching. Proc. IEEE Workshop Variational and Level Set Methods. 2001: 21-28
42 M. Fornefett, K. Rohr, and H. Stiehl. Elastic Registration of Medical Images Using Radial Basis Functions with Compact Support. Proc. IEEE Conf. Computer Vision and Pattern Recognition.1999, 1:1402-1409
43 Xiaolei Huang, Nikos Paragios and Dimitris N. Metaxas. Shape Registration in Implicit Spaces Using Information Theory and Free Form Deformations. IEEE Trans. on Pattern Analysis and Machine Intelligence. 2006, 28(8):1303-1318
44 D. Rueckert, L. Sonoda, C. Hayes, D. Hill, M. Leach, and D. Hawkes. Nonrigid Registration Using Free-Form Deformations: Application to Breast MR Images. IEEE Trans. Medical Imaging. 1999,8: 712-721
45 T. Sederberg and S. Parry. Free-Form Deformation of Solid Geometric Models. Proc. ACM SIGGRAPH. 1986: 151-160
46 Y. Gdalyahu and D. Weinshall. Flexible Syntactic Matching of Curves and Its Application to Automatic Hierarchical Classification of Silhouettes. IEEE Trans. Pattern Analysis and Machine Intelligence. 1999, 21(12): 1312-1328
47 A.E. Johnson and M. Hebert. Recognizing Objects by Matching Oriented Points. Proc. IEEE Conf. Computer Vision and Pattern Recognition. 1997:684-689
48 A. Latif-Amet, A. Ertu’zu’n, , A. Ercil. An efficient method for texture defect detection: sub-band domain co-occurrence matrices. Image and Vision Computing. 2000, 18: 543–553
49 S. Kim, M. H. Lee, and K. B.Woo. Wavelet analysis to defects detection in weaving processes. Proc. IEEE Int. Symp. Industrial Electronics. 1999, 3(6): 1406–1409
50 Jong Pil Yun; YoungSu Park; Boyeul Seo; Sang Woo Kim; Se Ho Choi; Chang Hyun Park; Ho Mun Bae; Hwa Won Hwang. Development of Real-time Defect Detection Algorithm for High-speed Steel Bar in Coil(BIC). SICE-ICASE. 2006:2495-2498
51 Kumar, A. Computer-Vision-Based Fabric Defect Detection: A Survey., IEEE Transactions on Industrial Electronics. 2008, 55(1):348-363
52 Hiroyuki Onishi,Shoji Tatsumi. A Pattern Defeat Inspection Method by Parallel Grayscale Image Comparison without Precise Image Alignment. IEEE 2002
53 V. Scotti, F. Roveri. Visual inspection of particle boards for quality assessment Piuri. IEEE International Conference on Image Processing, 2005, 3: 521-525
54 Michael S. Brown and W. Brent Seales. Image Restoration of Arbitrarily Warped Documents. IEEE Trans. on PAMI, Vol. 26, No. 10, 2004:1295-1306
55 Vittoria Bruni and Domenico Vitulano. A Generalized Model for Scratch Detection. IEEE Trans. Image Processing., Vol. 13, No. 1, 2004:44-50
56 Tsui, B.M.W., Lalush, D.S., Frey, E.C., Gullberg, G.T.. Evaluation of myocardial defect detection between parallel-hole and fan-beam SPECT using the Hotelling trace Wollenweber. IEEE Transactions on Nuclear Science. 1998, 45(4):2205-2210
57 Wollenweber. S.D.,Tsui, B.M.W.,Lalush, D.S.,Frey, E.C.,LaCroix, K.J., Gullberg, G.T.. Comparison of Hotelling observer models and human observers in defect detection from myocardial SPECT imaging. IEEE Transactions on Nuclear Science. 1999, 46(6):2098-2103
58 Mircea Ionescu and Anca Ralescu. Fuzzy Hamming Distance BasedBanknote Validator. IEEE International Conference on Fuzzy Systems. 2005:300-305
59 C. He, M. Girolami, and G. Ross. Employing optimized combinations of one-class classifiers for automated currency validation. Pattern Recognition 2004, 37(6):1085–1096.
60 Masaru Teranishi, T. Matsui, Sigeru Omatu and Toshihisa Kosaka. Neuro-classification of fatigued bill based on tensional acoustic signal. SMCIA 2005: 173-177
61 Teranishi Omatu and Kosaka. New and used bills classification for cepstrum patterns. IJCNN, 1999: 3978-3981
62 Teranishi Omatu and Kosaka. Classification of bill fatigued levels by feature-selected acoustic energy pattern using competitive neural network. IJCNN, 2001: 249-253
63 Teranishi Omatu and Kosaka. Neuro-classification of bill fatigue levels based on acoustic wavelet components. ICANN ,2002: 237-241
64梁友杰,人民币防伪技术及真伪鉴别,中国金融出版社, 2005.
65陈伟,赵艳华.银行纸币鉴伪系统设计.自动化技术与应用.2003,22(3):53-56
66张力,黄建同.关于第五套人民币2005年版纸币防伪特征的研究.警察技术, 2006, 6:50-52
67唐春晖,沈伟良.全自动人民币伪币鉴别仪的设计.仪表技术,2006, 3: 23-25
68党选举,原军胜.纸质传感器在点伪钞机中的应用.传感器技术, 1995, 1:55-58
69 Hiroshi Sako, Takashi Watanabe, Hiroto Nagayoshi, and Tatsuhiko Kagehiro. Self-Defense-Technologies for Automated Teller Machines. IEEE International Machine Vision and Image Processing Conference, 2007: 177-184.
70何佳兵,李习伦,刘松波,王希奎.纸币清分机系统的研究与开发,机电工程技术,2007,36(8): 29-32.
71 Daishi Suzuki. Banknote validating apparatus and method. U.S. Patent, Patent number: 7084416
72 Otsuka. bank note processing apparatus and bank note processing methodU.S. Patent, Patent number: 6659260
73 TienYuan Chien, Banknote receiver for ticket vendor , U.S. Patent, Patent number: 6983881
74 Recycling of Euro Banknotes: Framework for the detection of counterfeits and fitness sorting by credit institutions and other professional cash handlers. European central bank. Jan.2005
75 U.V.Chaudhari, J.Navratil. Multigrained Modeling with Pattern Specific Maximum Likelihood Transformations for Text-Independent Speaker Recognition. IEEE Transactions on Speech and Audio Processing. 2003, 11(1): 478-485
76 A. Hedelin , J. Skoglund. Vector Quantization Based on Gaussian Mixture Models. IEEE Trans. Speech and Audio Processing, 2000, 8 (4): 385-401
77 Martin H.C. Law, Mário A.T. Figueiredo and Anil K. Jain. Simultaneous Feature Selection and Clustering Using Mixture Models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2004, 26(9): 1154-1166
78 Mario A.T. Figueiredo, Anil K.Jain. Unsupervised Learning of Finite Mixture Models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002, 24(3):381-396
79 Constantinos Constantinopoulos. Michalis K. Titsias, and Aristidis Likas. Bayesian Feature and Model Selection for Gaussian Mixture Models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2006, 28(6): 1013-1018
80 Vapnik V. Estimation of Dependencies Based on Empirical Data. Springer, 1982
81 Vapnik V. Statistical Learning Theory. John Wiley, 1998
82 Ben Hamza, Hamid Krim, B. Karacali. Structural Risk Minimization Using Nearest Neighbor Rule. ICASSP, 2003: 161-165
83 John Shawe-Taylor, Peter L. Bartlett. Structural Risk Minimization Over Data-Dependent Hierarchies. IEEE Transactions on Information Theory. 1998, 44(5):347-358
84 Andrew Webb. Statistical Pattern Recognition. Oxford University Press, New York, 1999.
85 C. Papageorgiou, M. Oren, and T. Poggio. A general framework for objectdetection. International Conference on Computer Vision, 1998:2173-2178
86 A D. Roth, M. Yang, and N. Ahuja. A snowbased face detector. Neural Information Processing, 2000:490-495
87 Yoav Freund and Robert E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Computational Learning Theory, Eurocolt’95. Springer-Verlag, 1995:23–37.
88 S.Mori, C.Y.Suen and K.Yamamoto. Historical Review of OCR Research and Development. Proceedings of the IEEE. 1992, 80:1029-1058
89 R.Plamondon. On-line and Off-line Handwriting Recognition: a Comprehensive Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000, 22(1): 63-84
90 S.V. Rice, G. Nagy and T.A. Nartker. Optical Character Recognition: An illustrated guide to the frontier. Kluwer Academic Publishers, 1999
91 R. C. Gonzalez and P.Wintz, Digital Image Processing. Reading, MA: Addison -Wesley, 1987.
92 HengDa Cheng, Ying Sun. A hierarchical approach to color image segmentation using homogeneity, IEEE Trans on Image Processing, 2000, 9(12):2071~2082
93 Hanzi Wang, David Suter. Color Image Segmentation Using Global Information and Local Homogeneity. Proc. 7th Digital Image Computing: Techniques and Applications. 2003:89-98
94色彩学编写组,色彩学.科学出版社, 2001
95 R.W. G. Hunt.Measuring Color.2nd ed. Chichester. U.K. Ellis Horwood, 1987.
96 Luca Lucchese, Sanjit K. Mitra. A new class of chromatic filters for color image processing:theory and applications. IEEE Trans on Image Processing, 2004, 13(4): 534-548
97 F. L. Bookstein. Principal warps: Thin-plate splines and the decomposition of deformations. IEEE Trans. Pattern Anallsis Machine Intelligence. 1989, 11(6): 567-585.
98 M. H. Davis, A. Khotanzad, D. P. Flamig, and S. E. Harms. A physicsbased coordinate transformation for 3-D image matching. IEEE Trans. Medical Image. 1997, 16(5): 317–328.
99 S. Lee, G. Wolberg, K.-Y. Chwa, and S. Y. Shin. Image metamorphosis with scattered feature constraints. IEEE Trans. Visualization Comput. Graph. 1996,2(11) 337–354
100 S. Lee, G. Wolberg, and S. Y. Shin. Scattered data interpolation with multilevel B-splines. IEEE Trans. Visualization Comput. Graph.. 1997, 3(7): 228–244
101 E. Bardinet, L. D. Cohen, and N. Ayache,“Tracking and motion analysis of the left ventricle with deformable superquadrics,”Med. Image Anal.. 1996 1(2):129–149
102 Julia A. Schnabel, Christine Tanner, Andy D. Castellano-Smith, Andreas Degenhard, Martin O. Leach, D. Rodney Hose, Derek L. G. Hill, and David J. Hawkes. Validation of Nonrigid Image Registration Using Finite-Element Methods: Application to Breast MR Images. IEEE Transactions on medical imaging. 2003, 22(2): 238-247