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Reseach Article

Currency Recognition using SIFT

by S. A. Bhavani
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 167 - Number 9
Year of Publication: 2017
Authors: S. A. Bhavani
10.5120/ijca2017914368

S. A. Bhavani . Currency Recognition using SIFT. International Journal of Computer Applications. 167, 9 ( Jun 2017), 15-20. DOI=10.5120/ijca2017914368

@article{ 10.5120/ijca2017914368,
author = { S. A. Bhavani },
title = { Currency Recognition using SIFT },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2017 },
volume = { 167 },
number = { 9 },
month = { Jun },
year = { 2017 },
issn = { 0975-8887 },
pages = { 15-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume167/number9/27800-2017914368/ },
doi = { 10.5120/ijca2017914368 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:14:24.291631+05:30
%A S. A. Bhavani
%T Currency Recognition using SIFT
%J International Journal of Computer Applications
%@ 0975-8887
%V 167
%N 9
%P 15-20
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Currency recognition is an important area of pattern recognition. A system for the recognition of currency is one kind of intelligent system which is a very important need of the current automation systems in the modern world of today. Currency Recognition and is implemented to reduce human power to automatically recognize the amount monetary value of currency and without human supervision. The software interface that we are proposing here could be used for various currencies (we are using four in my paper). Many a times, currency notes are blurry or damaged; many of them have complex designs to enhance security. This makes the task of currency recognition very difficult. So it becomes very important to select the right features and proper algorithm for this purpose. The basic requirements for an algorithm to be considered as practically implementable are simplicity, less complexity, high speed and efficiency. Our main aim is to design an easy but efficient algorithm that would be useful for maximum number of currencies, because all currencies have different security features, making it a tough job to design one algorithm that could be used for recognition of all available currencies. Writing different programs for all is also a tedious job. The algorithm used in my paper is SIFT (Scale Invariant Feature Transform) algorithm. The aim of the paper is to recognize the currencies.

References
  1. N. Abbadeni, “Information retrieval from visual databases using multiple representations and multiple queries,” in Proc. ACM Symp. Appl.Comput., 2009, pp. 1523–1527.
  2. N. Abbadeni, “Perceptual image retrieval,” in Proc. Int. Conf. Vis. Inf. Syst., Amsterdam, Netherlands, 2005, pp. 259–268.
  3. N. Abbadeni, “Multiple representations, similarity matching, and results fusion for content-based image retrieval,” Multimedia Syst. J., vol. 10, no. 5, pp. 444–456, 2005.
  4. N. Abbadeni, “Content representation and similarity matching for texture- based image retrieval,” in Proc. 5th ACM Int. Workshop Multimedia Inf. Retrieval, Berkeley, CA, 2003, pp. 63–70.
  5. N. Abbadeni, “A new similarity matching measure: Application to texture- based image retrieval,” in Proc. 3rd Int. Workshop Texture Anal. Synth., Nice, France, 2003, pp. 1–6.
  6. N. Abbadeni, D. Ziou, and S. Wang, “Computational measures corresponding to perceptual textural features,” in Proc. 7th IEEE Int. Conf. Image Process., Vancouver, Canada, 2000, vol. 3, pp. 897–900.
  7. N. Abbadeni, D. Ziou, and S.Wang, “Autocovariance-based perceptual textural features corresponding to human visual perception,” in Proc. 15th IAPR/IEEE Int. Conf. Pattern Recognit., Barcelona, Spain, Sep. 3–8, 2000, vol. 3, pp. 901–904.
  8. M. Amadasun and R. King, “Textural features corresponding to textural properties,” IEEE Trans. Syst., Man Cybern., vol. 19, no. 5, pp. 1264–1274, Sep.-Oct. 1989.
  9. J. Ashley, R. Barber, M. Flickner, J. Hafner, D. Lee, W. Niblack, and D. Petkovic, “Automatic and semi-automatic methods for image annotation and retrieval in QBIC,” in Proc. SPIE Conf. Storage Retrieval for Image and Video Databases, 1995, vol. 2420, pp. 24–35.
  10. J. R. Bergen and E. H. Adelson, “Early vision and texture perception,” Nature, vol. 333, no. 6171, pp. 363–364, May 1988.
  11. P. Brodatz, Textures: A Photographic Album for Artists and Designers. New York: Dover, 1966.
  12. R. Datta, D. Joshi, J. Li, and J. Z. Wang, “Image retrieval: Ideas, influences, and trends of the new age,” ACM Trans. Comput. Surv., vol. 40, no. 2, p. 60, 2008.
  13. M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, and B. Dom et al., “Query by image and video content: The QBIC system,” IEEE Computer, vol. 28, no. 9, pp. 23–32, Sep. 1995.
  14. J. C. French, A. C. Chapin, and W. N. Martin, “An application of multiple viewpoints to content-based image retrieval,” in Proc. ACM/IEEE Joint Conf. Digital Libraries, 2003, pp. 128–130.
  15. J. C. Gower, “A general coefficient of similarity and some of its properties,” Biometrics J., vol. 27, pp. 857–874, 1971.
Index Terms

Computer Science
Information Sciences

Keywords

Currency Recognition SIFT Digital Image Processing.