期刊文献+

基于DSP的纸币光变油墨自动识别的研究 被引量:2

Research of Automatic Recognition of Bank Note's Photo-induced Discoloration Printed Ink Based on DSP
下载PDF
导出
摘要 在同一光源不同照射角度下,光变油墨数字颜色存在着明显的变化,这是进行纸币的真伪识别的重要方法之一,区别于人工识别,在纸币真伪鉴别上提出了针对光变油墨技术的机器识别方法,纸币机器自动识别系统主要由高速数字信号处理器(DSP)、现场可编程门阵列(FPGA)和彩色CCD摄像机组成。该系统由FPGA对视频数据流进行预处理并且控制多个存储区有序切换,完成纸币图像数据的实时采集存储;针对图像数据的特点,基于片上可编程系统(SOPC)的概念,FPGA中集成了Nios软核实现并行处理,DSP完成纸币的识别过程。通过实验验证了针对光变油墨技术的机器识别方法的可行性和正确性。 The color of the figures printed with photo-induced discoloration ink has a special change when the light irradiates a bank note from different angles. This is an important method to identify if a bank note is faked or not. In this paper, an automatic recognition method focusing on photo-induced discoloration printed ink technique is proposed to recognize the fakeness of bank notes. The bank note automatic recognition system mainly consists of DSP, FPGA and color CCD camera. The system pre-handles video data stream by FPGA, sequentially switches among different memory areas and completes the real-time collection and storage of hank note image data. According to the characteristics of the image data, based on the concept of SOPC, FPGA integrates Nios soft core and implements parallel processing. Then DSP completes the recognition process of a bank note. The feasibility and effectiveness of the proposed automatic recognition method is verified by various experiments.
出处 《中国图象图形学报》 CSCD 北大核心 2009年第5期950-956,共7页 Journal of Image and Graphics
基金 国家自然科学基金项目(60274009)
关键词 纸币识别 光变油墨 DSP FPGA SOPC bank note recognition, photo-induced discoloration printed ink, DSP(digital signal processor), FPGA(field-programmable gate array) , SOPC( system on a programmable chip)
  • 相关文献

参考文献11

二级参考文献24

  • 1方培生.鉴别纸币真假的数码化InSb传感器的研制[J].传感技术学报,2002,15(2):177-179. 被引量:6
  • 2雷玉堂,光电检测技术,1997年 被引量:1
  • 3贺安之,现代传感器原理及应用,1995年 被引量:1
  • 4胡鸿璋,应用光学原理,1993年 被引量:1
  • 5Shapiro J M.Embedded image coding using zerotrees of wavelet coefficients[J].IEEE Transactions on Signal Processing,1993,41(12):3445 -3462. 被引量:1
  • 6Said A,Pearlman W.A new fast and efficient image codec based on set partitioning in hierarchical trees[J].IEEE Transactions on Circuits and System Video Technology,1996,6 (3):243 -249. 被引量:1
  • 7Xiong Z,Ramchandran K,Orchard M T.Space frequency quantization for wavelet image coding[J].IEEE Transactions on Image Processing,1997,6 (5):677 - 693. 被引量:1
  • 8Creusere C D.A new method of robust image compression based on the embedded zero2tree wavelet algorithm[J].IEEE Transactions on Image Processing,1997,6(10):1436 - 1442. 被引量:1
  • 9Luo J,Chen C W,Parker K J,et al.A scene adaptive and signal adaptive quantization for subband image and video compression using wavelets[J].IEEE Transactions on Image Processing,1997,7(2):343 - 357. 被引量:1
  • 10Watson A B,Yang G Y,Solomon J A,et al.Visibility of wavelet quantization noise[J].IEEE Transactions on Image Processing,1997,6(8):1164 -1175. 被引量:1

共引文献37

同被引文献41

  • 1陈宁.利用现代物证分析技术推断毒品的来源[J].政法学刊,2004,21(6):71-72. 被引量:5
  • 2衡磊,丁永生.应用PIXE技术对书写字迹的分析[J].光谱实验室,2005,22(5):928-932. 被引量:3
  • 3Ionescu, M, Ralescu, A. Fuzzy hamming distance based banknote validator [ C ]. Proceedings of IEEE Conference on Fuzzy Sys- tems, Reno NV, USA, IEEE :2005 :300-305. 被引量:1
  • 4Chao He, Mark Girolami, Gany Ross. Employing optimized combination of one-class for automated currency validation [J]. Pat- tern Recognition, 2004, 37 (6) : 1085-1096. 被引量:1
  • 5Law M. H. C, Figueiredo M. A. T, Jain A. K. Simultaneous feature selection and clustering using mixture models [ J ]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2004, 26(9) :1154-1166. 被引量:1
  • 6Takeda, F, Omatu, S. High speed paper currency recognition by neural networks [ J ]. IEEE Transactions on Neural Networks, 1995, 6( 1 ) :73-77. 被引量:1
  • 7Choi E, Lee J, Yoon J. Feature Extraction for Bank Note ClassificationUsing Wavelet Transform[ C]. IEEE International Confer- ence on Pattern Recognition, Hongkong, China, 2006:934-937. 被引量:1
  • 8Ye Jin, Ling Song, Xianglong Tang, etal. A Hierarchical Approach for Banknote Image Processing Using Homogeneity and FFD Model [ J ]. IEEE Transactions on Signal Processing Letters. 2008.15 (3) :425-428. 被引量:1
  • 9Omatu. S, Yoshioka. M, Kosaka. Y. Reliable Banknote Classification Using Neural Networks [ C ]. Proceedings of IEEE Conference on Advanced Engineering Computing and Application in Sciences,2009:35-40. 被引量:1
  • 10Rashid. A, Prati. A, Cucchiara. R. A real-time embedded solution for skew correction in banknote analysis [ C ]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops,2011:42-49. 被引量:1

引证文献2

二级引证文献6

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部