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Detection of Counterfeit Coins and Assessment of Coin Qualities.

Title:

Detection of Counterfeit Coins and Assessment of Coin Qualities.

Sun, Ke (2015) Detection of Counterfeit Coins and Assessment of Coin Qualities. Masters thesis, Concordia University.

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Abstract

Due to the proliferation of fake money these days, detection of counterfeit coins with high accuracy is in strong demand, yet not much research has been conducted in this field. The objective of this thesis is to introduce modern computer vision techniques and machine intelligence to differentiate real coins and fake ones with high precision, based on visual aspects.
To that end, a high-resolution scanning device – IBIX Trax is deployed to sample the coin images. On top of that, three visual aspects are thoroughly inspected, namely lettering, images and texture.
Six features are extracted from letterings, i.e. stroke width, contour smoothness, lettering height, lettering width, relative angle, and relative distance. As for classification, a hierarchical clustering – max spacing K-clustering—is adopted. Our experimental results show that the fake coins and real ones are totally separable based on these features.
As for images, we propose a novel shape feature— angle-distance. After images are segmented, a vector of size 360*1 is deployed to represent each shape. For classification, a dissimilarity measurement is used to quantize the difference between two shapes. The results show it can recognize the fake coins successfully.
As for texture, a cutting-edge feature maximum stable extremal region is adopted to automatically detect the holes and indents on the coin surface. Parameters associated with this feature are adjusted in the experiments. The detection results show this feature can be used as an indicator for assessing the qualities of coins.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:Sun, Ke
Institution:Concordia University
Degree Name:M. Comp. Sc.
Program:Computer Science
Date:July 2015
Thesis Supervisor(s):Suen, Ching Y.
ID Code:980253
Deposited By: KE SUN
Deposited On:03 Nov 2015 17:10
Last Modified:18 Jan 2018 17:51

References:

[1] Goldsborough, R. Counterfeit Coin Detection. http://rg.ancients.info/guide/counterfeits.html, 2013.
[2] Inksure Technologies. Coin Anti-Counterfeiting. http://www.inksure.com/banknotesecurity/
254-coin-anti-counterfeiting, 2014.
[3] The Royal Mint, UK. £1 Counterfeit Coins. http://www.royalmint.com/discover/uk-coins/counterfeit-one-pound-coins.
[4] Australian Federal Police. Policing: Counterfeit Currency. http://www.afp.gov.au/policing/
counterfeit-currency.
[5] Wikipedia. Currency Detector. https://en.wikipedia.org/wiki/Currency_detector.
[6] Ultra Electronics Forensic Technology Ltd. IBIS BULLETTRAX-3D: The Only System to Automatically Image & Compare 3D images of Fired Bullets, 2009.
[7] Ultra Electronics Forensic Technology Ltd. IBIS BULLETTRAX-3D: Benefits of Combining 2D and 3D Image, 2009.
[8] Sun, K., Feng, B.-Y., Atighechian, P., Levesque, S., Sinnott, B. & Suen, C. Y. Detection of Counterfeit Coins Based on Shape and Letterings Features (in press). Proceedings of 28th ISCA International Conference on Computer Applications in Industry and Engineering, San Diego, USA, Oct. 2015.
[9] Reisert, M., Ronneberger, O., Burkhardt, H. A Fast and Reliable Coin Recognition System. Proceedings of 29th DAGM Symposium, vol. 4731, pp. 415-424, Heidelberg, Germany, Sep.12-14, 2007.
[10] Tsai, D.M., Chiang, C.H. Rotation-Invariant Pattern Matching Using Wavelet Decomposition. Pattern Recognition Letters, vol. 23, pp. 191-201. Jan. 2002.
[11] Wei, K.P., He, B., Wang, F., Zhang, T., & Ding Q.J. A Novel Method for Classification of Ancient Coins Based on Image Textures. Proceedings of the Second Workshop on Digital Media and Its Application in Museum & Heritage, pp. 63-66, 2007.
[12] Shen, L., Jia, S., Ji, Z., Chen, W.S. Extracting Local Texture Features for Image-based Coin Recognition. IET Image Processing, vol. 5, pp. 394-401, Aug. 2011.
[13] Huber, R., Ramoser, H., Mayer, K., Penz, H., Rubik, M. Classification of Coins Using An Eigen-space Approach. Pattern Recognition Letters, vol. 26, pp. 61-75, Jan. 2005.
[14] Nolle, M., Penz, H., Pubik, M., Mayer, K., Hollander, I., Geanec, R. A New Coin Recognition and Sorting System. Proceedings of the 7th International Conference on Digital Image Computing Techniques and Applications, pp. 329-338, Sydney, Australia, Dec. 2003.
[15] Bremananth, R., Balaji, B., Sankari, M., Chitra, A. A New Approach To Coin Recognition Using Neural Pattern Analysis. Proceedings of IEEE INDICON, pp. 366–370, India, Dec. 2005.
[16] Reisert, M., Ronneberger, O., Burkhardt, H. An Efficient Gradient Based Registration Technique for Coin Recognition. Proceedings of the MUSCLE CIS Coin Competition Workshop, pp. 19–31, Berlin, German, Sep. 2006.
[17] Van der Maaten, L. J. P., Postma, E. O. Towards Automatic Coin Classification. Digital Cultural Heritage - Essential for Tourism, Oestereichische Computer Gesellschaft, pp. 19–26, 2006.
[18] Modi, S., Bawa, S. Automated Coin Recognition System using ANN. International Journal of Computer Applications, vol.26, pp. 13-18, Jul. 2011.
[19] Van der Maaten, L. J. P., Boon, P. J. COIN-O-MATIC: A Fast System for Reliable Coin Classification. Proceedings of the MUSCLE CIS Coin Competition Workshop, Berlin, German, pp. 7–17, 2006.
[20] Takacs, G., Chandrasekhar, V., Tsai S.S., Chen, D., Grzeszczuk, R., Girod, B. Fast Computation of Rotation-Invariance Image Features by an Approximate Radial Gradient Transform. IEEE Trans Image Process, vol. 22, pp. 2970-2982. Aug. 2013.
[21] Feng, B.-Y., Sun, K., Atighechian, P., Suen, C.Y. Computer Recognition and Evaluation of Coins, in press, Chen, C.H. (ed.). Handbook of Pattern Recognition and Computer Vision, 5th Edition for World Scientific Publishing Publication, January 2016.
[22] Hough, P.V.C. A Method and Means for Recognizing Complex Patterns, U.S. Patent 3,069,654, Dec. 1962.
[23] Duda, R.O., Hart, P.E. Use of Hough Transformation to Detect Lines and Curves in Pictures. Communications of the ACM, vol. 15, pp. 11-15, Jan. 1972.
[24] Otsu, N. A Thresholding Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, SMC-9, vol. 9, pp. 62-66, Jan. 1979.
[25] Niblack, W. An Introduction to Digital Image Processing. Strandberg Publishing Company, Birkeroed, Denmark, 1985.
[26] Connected components Labeling. http://homepages.inf.ed.ac.uk /rbf/HIPR2 /label.htm, 2003.
[27] Ntirogiannis, K., Gatos, B., Pratikakis, I. A Modified Adaptive Logical Level Binarization Technique for Historical Document Images. Proceedings of the 10th International Conference on Document Analysis and Recognition, IEEE Computer Society, Barcelona, Spain, pp.1171-1175, 2009.
[28] Rafael C.G., Richard E.W. Digital Image Processing, 3rd edition, pp. 628-638, Prentice Hall, 2007.
[29] Jain, A.k. Data Clustering : 50 Years Beyond K-Means. Pattern recognition letters, issue. 31, pp. 651-666, 2010.
[30] Asano, T., Bhattacharya, B., Keil, M., and Yao F. Clustering algorithms based on minimum and maximum spanning trees. Proceedings of the 4th Annual Symposium on Computational Geometry, Urbana Champaign, USA, pp. 252–257, 1988.
[31] Preparata, F., Shamos, M. Computational Geometry: An Introduction. Springer-Verlag, New York, USA, 1985.
[32] Zahn, C.T. Graph-theoretical Methods for Detecting and Describing Gestalt Clusters. IEEE Transactions on Computers, vol. C-20, pp. 68-86, Jan. 1971.
[33] Grygorash, O., Zhou, Y., Jorgensen, Z. Minimum Spanning Tree Based Clustering Algorithms. Proceedings of the 18th International Conference on Tools with Artificial Intelligence, Washington D.C., USA, pp. 73-81, Nov. 2006.
[34] Cormen, T.H., Leiserson, C., Rivest, R.L., Stein, C. Introduction to Algorithms, 3rd edition. The MIT press, Cambridge Massachusetts, USA, 2009.
[35] Kruskal, J. On the Shortest Spanning Subtree and the Traveling Salesman Problem. Proceedings of the American Mathematical Society, vol. 7, pp. 48–50, 1956.
[36] Xu, Y., Olman, V. and Xu, D. Minimum spanning trees for gene expression data clustering. Genome Informatics, vol. 12, pp. 24–33, 2001.
[37] Loncaric, S. A Survey of Shape Analysis Techniques. Pattern Recognition, vol. 31, pp. 983-1001, 1998.
[38] Materka, A., Strzelecki, M. Texture Analysis Methods – A Review. Institute of Electronics, Lodz, Poland, 1998.
[39] Tuytelaars, T., Mikolajczyk, K. Local Invariant Feature Detectors: A Survey. Foundations and Trends in Computer Graphics and Vision, vol. 3, pp. 177-280, Jan. 2008.
[40] D. Chetverikov and J. Matas. Periodic Textures as Distinguished Regions for Wide-Baseline Stereo Correspondence. In Proceedings of the 2nd International Workshop on Texture Analysis and Synthesis, Copenhagen, Denmark, pp. 25–30, 2002.
[41] Dorko, G., Schmid, C. Selection of Scale Invariant Neighborhoods for Object Class Recognition. Proceedings of the 9th International Conference on Computer Vision, Nice, France, pp. 634–640, Oct. 2003. 

[42] Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L. A Comparison of Affine Region Detectors. International Journal of Computer Vision, vol. 65, pp. 43–72, 2005.
[43] Forssen, P.-E., Lowe, D.G. Shape Descriptors for Maximally Stable Extremal Regions. Proceedings of IEEE 11th International Conference on Computer Vision, Rio de Janeiro, Brazil, pp. 1-8, Oct. 2007.
[44] Matas, J., Chum, O., Urban, M., Pajdla, T. Robust Wide Baseline Stereo from Maximally Stable Extremal Regions. Image and Vision Computing, vol. 22, pp. 761-767, Sep. 2004.
[45] S. Obdrzalek, Matas, J. Object Recognition Using Local Affine Frames on Maximally Stable Extremal Regions. Toward Category-Level Object Recognition, vol. 4170, pp. 83-104, 2006.
[46] Forssen, P.-E. Maximally Stable Colour Regions for Recognition and Matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, Minnesota, USA, Jun. 2007.
[47] Donoser, M., Bischof, H. Efficient Maximally Stable Extremal Region (MSER) Tracking. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 553-560, New York, USA, Jun. 2006.
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