三维条码印刷与识别技术研究
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摘要
条码技术自出现以来,发展十分迅速,极大地提高了数据采集和信息处理速度,为管理的科学现代化做出了巨大贡献。随着条码应用技术的推广,人们对条码的信息容量、安全性、识别率等提出了更高要求。近年来由于彩色印刷设备、采集设备日趋成熟和低成本化,人们在二维条码的基础上加入颜色信息进行扩展,形成了三维条码概念。作为新的信息存储和识别技术,三维条码自诞生起就得到了很多国家的关注。美日等国不仅将其应用于公安、外交、军事等部门的证件管理,还致力于条码新技术的研发和相关标准的制定,希望研制出更大信息量的码型结构以实现进一步应用需求。
三维条码在水平和垂直方向上结合模块颜色变化和纵向排列,能在有限的几何空间内表示出更多信息,达到提高信息容量的目的。它是实现信息存储和识别的有效工具,能详细地描述出商品特征。除具备二维条码的基本特征外,三维条码还具备信息容量大、可靠性高、纠错能力强、可记载复杂数据信息,如图像资料等。
本文以Data Matrix二维条码为例,通过研究其数据信息的编码生成过程,对条码模块引入颜色信息,在不改变平面空间大小的情况下增加了存储的信息量,实现了能在有限的空间内描述出更多商品信息的要求。并通过实际条码生成的例子,研究如何实现三维条码的生成与识别系统,研究适合实际印刷需要的色彩空间选择方法,设计彩色编码方案。为了更好地实现图像分割效果和提高识别率,提出了面向三维条码图像固定颜色自适应阈值Canny边缘检测算法。并通过一个实验系统验证了该算法的可行性。
Since the invention of barcode, it has been developing very quickly and greatly improved the data collection and speed of information process. It has made great contributions to the management of modern science. With the application of barcode technologies, people meet a higher requirement for barcode on its information capacity, security, recognition rate etc. In recent years, with the low cost and mature of color print and acquisition equipments, people put forward a concept of three-dimensional barcode by adding color information to the two-dimensional barcode. As a new technology of information storage and identification, since the birth of three-dimensional barcode many countries pay great attention to it. The United States and Japan not only applied it to the document management of departments, such as public security, diplomatic service, military branches etc, but also committed to the development of new technologies and related standards in order to meet the further requirements of greater amount of information and pattern structure.
With the modules color change and vertical arrangement, three-dimensional barcode can express more information in a limited space at the horizontal and vertical directions for the purpose of improving the information capacity. It is an effective tool to achieve information storage and identification and can describe the product features in detail. Besides the basic features of two-dimensional barcode, three-dimensional barcode has many features, such as higher information capacity, great reliability, nice error correction ability, and can record complex data, such as image data.
Based on the Data Matrix two-dimensional barcode and by studying the data coding process, we add color information to the module in order to increase the storage of information without changing the flat space and achieve the purpose of describing more product information in the limited space. By actual examples of barcode generation, this article discussed how to achieve the generation and recognition system of three-dimensional barcode, studied the color space selection methods suitable for the actual print, and designed a color coding scheme. A fixed-color adaptive threshold Canny edge detection algorithm based on three-dimensional bar code image is released in order to achieve effective image segmentation and recognition rate. The experiment shows that the algorithm is feasible.
三维条码在水平和垂直方向上结合模块颜色变化和纵向排列,能在有限的几何空间内表示出更多信息,达到提高信息容量的目的。它是实现信息存储和识别的有效工具,能详细地描述出商品特征。除具备二维条码的基本特征外,三维条码还具备信息容量大、可靠性高、纠错能力强、可记载复杂数据信息,如图像资料等。
本文以Data Matrix二维条码为例,通过研究其数据信息的编码生成过程,对条码模块引入颜色信息,在不改变平面空间大小的情况下增加了存储的信息量,实现了能在有限的空间内描述出更多商品信息的要求。并通过实际条码生成的例子,研究如何实现三维条码的生成与识别系统,研究适合实际印刷需要的色彩空间选择方法,设计彩色编码方案。为了更好地实现图像分割效果和提高识别率,提出了面向三维条码图像固定颜色自适应阈值Canny边缘检测算法。并通过一个实验系统验证了该算法的可行性。
Since the invention of barcode, it has been developing very quickly and greatly improved the data collection and speed of information process. It has made great contributions to the management of modern science. With the application of barcode technologies, people meet a higher requirement for barcode on its information capacity, security, recognition rate etc. In recent years, with the low cost and mature of color print and acquisition equipments, people put forward a concept of three-dimensional barcode by adding color information to the two-dimensional barcode. As a new technology of information storage and identification, since the birth of three-dimensional barcode many countries pay great attention to it. The United States and Japan not only applied it to the document management of departments, such as public security, diplomatic service, military branches etc, but also committed to the development of new technologies and related standards in order to meet the further requirements of greater amount of information and pattern structure.
With the modules color change and vertical arrangement, three-dimensional barcode can express more information in a limited space at the horizontal and vertical directions for the purpose of improving the information capacity. It is an effective tool to achieve information storage and identification and can describe the product features in detail. Besides the basic features of two-dimensional barcode, three-dimensional barcode has many features, such as higher information capacity, great reliability, nice error correction ability, and can record complex data, such as image data.
Based on the Data Matrix two-dimensional barcode and by studying the data coding process, we add color information to the module in order to increase the storage of information without changing the flat space and achieve the purpose of describing more product information in the limited space. By actual examples of barcode generation, this article discussed how to achieve the generation and recognition system of three-dimensional barcode, studied the color space selection methods suitable for the actual print, and designed a color coding scheme. A fixed-color adaptive threshold Canny edge detection algorithm based on three-dimensional bar code image is released in order to achieve effective image segmentation and recognition rate. The experiment shows that the algorithm is feasible.
引文
[1] Sriram T, Rao V K. Applications of Barcode Technology in Automated Storage Retrieval Systems. IECON Proceedings,1996,1:5-10
[2]刘宁钟,杨静宇.三维条码的编码理论和设计[J].计算机学报, 2007, 30(4) : 686-692
[3] AIM. Uniform Symbology Specification: PDF417.1994
[4] International Organization for Standardization. ISO/IEC 16022. Information Technology International Symbology Specification: Data Matrix, 2000
[5] Pavlidis T, Swartz J. Fundamentals of bar code information theory. IEEE Computer, 1990, 23(4):74286
[6] Shellhammer S J, Goren David P, Pavlidis T. Novel signal processing techniques in barcode scanning. IEEE Robotics and Automation Magazine, 1999, 6(1):57265
[7] Normand N,Viard-Guadin C. Two Dimensional Bar Code Reader. Proceedings of SPIE, 1994: 125-132
[8] Pavlidis T, Swartz J, Wang Y P. Information encoding with two dimensional bar codes. IEEE Computer, 1992, 25(6):18228
[9] F J MacWilliams, N J A Sloanc. Theory of Error Correcting Codes. Amsterdam: North Holland, 1977
[10] R E Blahut, Theory and Practice of Error Control Codes. Reading, MA: Addison Wesley, 1983
[11] Dony RD, Wesolkowski S. Edge Detection On Color Images Using RGB Vector Angles[A]. Proceedings of the 1999 IEEE Canadian Conference on Electrical and Computer Engineering Shaw Conference Center[C]. Edmonton, Alberta, Canada,May 9-12,1999:633-638
[12]叶国军,王绍棣.基于CIELab颜色空间和颜色分布信息熵的图像检索[J].计算机工程与应用, 2007, 29(3):98-100
[13] Lukac R. Adaptive color image filtering based on center-weighted vector directional filters[J]. Multidimensional Systems and Signal Processing,2004,15(2):169-196.
[14] LIANG Xiao long, FENG Jin fu, LI Qing. A swarm intelligence optimization for particle filter[C]. The 7th World Congress on Intelligent Control and Automation. 2008: 1986-1991
[15] Yun-Seok Lee, Han-Suh Koo, Chang-Sung Jeong. A straight line detection using principalcomponent analysis. Pattern Recognition Letters[J], 2006, 27(14):1744-1754
[16] L. Pires,P. De Smet,I. Bruyland. Line extraction with the use of an automatic gradient threshold technique and the Hough transform[A]. International Conference on Image Processing. Vancouver, IEEE, 2000, 909-912.
[17]李文举,梁德群,张旗,王演。基于方向区域距离测度的彩色边缘检测方法[J].计算机应用,2003年1月
[18]蔡灿辉,丁润涛。超复数空间彩色边缘检测器的实现.数据采集与处理,2002年第17卷第4期
[19] FAN J P, YAU D K Y. Automatic image segmentation by integrating color-edge extraction and seeded region growing[J]. IEEE Transactions on Image Processing, 2001, 10(10): 1454-1466
[20] R.Deriche. Using Canny's Criteria to Derive a Recursively Implemented Optimal Edge Detector. Int.J.Computer Vision, Vol.l, No.2, 1987
[21] Huertas A, Medioni G. Detection of Intensity Changes with Subpixel Accuracy Using Laplacian-Gaussian Masks. IEEE Trans. PAMI,1986,8(5):651-664
[22] Zhu L. A fundamental solution for the Laplace operator on the quaternionic Heisenberg group. Acta Mathematica Scientia, 2002, 22B(3):369-378
[23] Joseph E, Pavlidis T. Bar Code Waveform Recognition Using Peak Locations. IEEE Trans. PAMI,1994,16(6):630-640
[24] SCHARCANSKI J, VENETSANOPOULOS A. N. Edge detection of color images using directional operators[J]. IEEE Transaction on Circuits and Systems for Video Technology, 1997, 7(2):397.
[25] CANNY J. A computational approach to edge detection[J]. IEEE Trans Pattern Analysis and Machine Intelligence,1986, 8(6):679-698.
[26] Lihui Jiang, Yanying Guo. Image edge detection based on adaptive weighted morphology [J]. Chinese Optics Letters, 2007,5(2):77-78
[27] Bao P, Zhang L. Canny edge detection enhancement by scale multiplication[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005,27(9):1485-1490
[28] Okol′nishnikova L V. Polynomial algorithm for recognition of bar codes. Pattern Recognition and Image Analysis, 2001, 11(2):3612364
[29] Naik Sarif Kumar, Murthy C A. Hue Preserving color image enhancement without gamutProblem[J]. IEEE Transactions on Image Processing, 2003, 12(12):1591-1598
[30] Jesús Angulo, Jean Serra. Modelling and segmentation of colour images in polar representations[J]. Image and Vision Computing. 2007,25(4):475-495
[31] Harro Stokman, Theo Gevers. Selection and fusion of color models for image feature detection[J]. IEEE Transactions on Pattern Analysis and Machine
王含锁,刘小丹三维条码印刷与识别技术研究计算机工程与设计2011,1
[2]刘宁钟,杨静宇.三维条码的编码理论和设计[J].计算机学报, 2007, 30(4) : 686-692
[3] AIM. Uniform Symbology Specification: PDF417.1994
[4] International Organization for Standardization. ISO/IEC 16022. Information Technology International Symbology Specification: Data Matrix, 2000
[5] Pavlidis T, Swartz J. Fundamentals of bar code information theory. IEEE Computer, 1990, 23(4):74286
[6] Shellhammer S J, Goren David P, Pavlidis T. Novel signal processing techniques in barcode scanning. IEEE Robotics and Automation Magazine, 1999, 6(1):57265
[7] Normand N,Viard-Guadin C. Two Dimensional Bar Code Reader. Proceedings of SPIE, 1994: 125-132
[8] Pavlidis T, Swartz J, Wang Y P. Information encoding with two dimensional bar codes. IEEE Computer, 1992, 25(6):18228
[9] F J MacWilliams, N J A Sloanc. Theory of Error Correcting Codes. Amsterdam: North Holland, 1977
[10] R E Blahut, Theory and Practice of Error Control Codes. Reading, MA: Addison Wesley, 1983
[11] Dony RD, Wesolkowski S. Edge Detection On Color Images Using RGB Vector Angles[A]. Proceedings of the 1999 IEEE Canadian Conference on Electrical and Computer Engineering Shaw Conference Center[C]. Edmonton, Alberta, Canada,May 9-12,1999:633-638
[12]叶国军,王绍棣.基于CIELab颜色空间和颜色分布信息熵的图像检索[J].计算机工程与应用, 2007, 29(3):98-100
[13] Lukac R. Adaptive color image filtering based on center-weighted vector directional filters[J]. Multidimensional Systems and Signal Processing,2004,15(2):169-196.
[14] LIANG Xiao long, FENG Jin fu, LI Qing. A swarm intelligence optimization for particle filter[C]. The 7th World Congress on Intelligent Control and Automation. 2008: 1986-1991
[15] Yun-Seok Lee, Han-Suh Koo, Chang-Sung Jeong. A straight line detection using principalcomponent analysis. Pattern Recognition Letters[J], 2006, 27(14):1744-1754
[16] L. Pires,P. De Smet,I. Bruyland. Line extraction with the use of an automatic gradient threshold technique and the Hough transform[A]. International Conference on Image Processing. Vancouver, IEEE, 2000, 909-912.
[17]李文举,梁德群,张旗,王演。基于方向区域距离测度的彩色边缘检测方法[J].计算机应用,2003年1月
[18]蔡灿辉,丁润涛。超复数空间彩色边缘检测器的实现.数据采集与处理,2002年第17卷第4期
[19] FAN J P, YAU D K Y. Automatic image segmentation by integrating color-edge extraction and seeded region growing[J]. IEEE Transactions on Image Processing, 2001, 10(10): 1454-1466
[20] R.Deriche. Using Canny's Criteria to Derive a Recursively Implemented Optimal Edge Detector. Int.J.Computer Vision, Vol.l, No.2, 1987
[21] Huertas A, Medioni G. Detection of Intensity Changes with Subpixel Accuracy Using Laplacian-Gaussian Masks. IEEE Trans. PAMI,1986,8(5):651-664
[22] Zhu L. A fundamental solution for the Laplace operator on the quaternionic Heisenberg group. Acta Mathematica Scientia, 2002, 22B(3):369-378
[23] Joseph E, Pavlidis T. Bar Code Waveform Recognition Using Peak Locations. IEEE Trans. PAMI,1994,16(6):630-640
[24] SCHARCANSKI J, VENETSANOPOULOS A. N. Edge detection of color images using directional operators[J]. IEEE Transaction on Circuits and Systems for Video Technology, 1997, 7(2):397.
[25] CANNY J. A computational approach to edge detection[J]. IEEE Trans Pattern Analysis and Machine Intelligence,1986, 8(6):679-698.
[26] Lihui Jiang, Yanying Guo. Image edge detection based on adaptive weighted morphology [J]. Chinese Optics Letters, 2007,5(2):77-78
[27] Bao P, Zhang L. Canny edge detection enhancement by scale multiplication[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005,27(9):1485-1490
[28] Okol′nishnikova L V. Polynomial algorithm for recognition of bar codes. Pattern Recognition and Image Analysis, 2001, 11(2):3612364
[29] Naik Sarif Kumar, Murthy C A. Hue Preserving color image enhancement without gamutProblem[J]. IEEE Transactions on Image Processing, 2003, 12(12):1591-1598
[30] Jesús Angulo, Jean Serra. Modelling and segmentation of colour images in polar representations[J]. Image and Vision Computing. 2007,25(4):475-495
[31] Harro Stokman, Theo Gevers. Selection and fusion of color models for image feature detection[J]. IEEE Transactions on Pattern Analysis and Machine
王含锁,刘小丹三维条码印刷与识别技术研究计算机工程与设计2011,1