A Speeded up Robust Scale-Invariant Feature Transform Currency Recognition Algorithm
Description
All currencies around the world look very different from each other. For instance, the size, color, and pattern of the paper are different. With the development of modern banking services, automatic methods for paper currency recognition become important in many applications like vending machines. One of the currency recognition architecture’s phases is Feature detection and description. There are many algorithms that are used for this phase, but they still have some disadvantages. This paper proposes a feature detection algorithm, which merges the advantages given in the current SIFT and SURF algorithms, which we call, Speeded up Robust Scale-Invariant Feature Transform (SR-SIFT) algorithm. Our proposed SR-SIFT algorithm overcomes the problems of both the SIFT and SURF algorithms. The proposed algorithm aims to speed up the SIFT feature detection algorithm and keep it robust. Simulation results demonstrate that the proposed SR-SIFT algorithm decreases the average response time, especially in small and minimum number of best key points, increases the distribution of the number of best key points on the surface of the currency. Furthermore, the proposed algorithm increases the accuracy of the true best point distribution inside the currency edge than the other two algorithms.
Files
10009047.pdf
Files
(430.1 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:2e11bc70bd634b5b3865b627d90849f4
|
430.1 kB | Preview Download |
Additional details
References
- A. Ahmadi and M. Manzoor, "Recognition System for Pakistani Paper Currency", Research Journal of Applied Sciences, Engineering and Technology, Vol. 6, No. 16, 3078-3085, Sep. 2013.
- R. Choras, "Image Feature Extraction Techniques and Their Applications for CBIR and Biometrics Systems", International Journal of Biology and Biomedical Engineering, Vol. 1, No. 1, 2007.
- V. Jain, and R. Vijay, "Indian Currency Denomination Identification Using Image Processing Technique", International Journal of Computer Science and Information Technologies, Vol. 4, No.1, pp. 126 -128, 2013.
- N. Paisios, A. Rubinsteyn, and L. Subramanian, V. Vyas, "Recognizing Currency Bills Using a Mobile Phone: An Assistive Aid for the Visually Impaired", User Interface Software and Technology, CA, USA, Oct. 2011.
- F. Hasanuzzaman, X. Yang, Y. Tian, "Robust and Effective Component-based Banknote Recognition by SURF Features", Wireless and Optical Communications Conference 20th Annual, Newark, NJ,1-6, April 2011.
- H. Bay, A. Ess , T. Tuytelaars, and L. Gool, "Speeded-Up Robust Features (SURF)", Computer Vision and Image Understanding: Similarity Matching in Computer Vision and Multimedia, Vol. 110, No. 3, pp. 346–359, June 2008.
- H. Aggarwal, and P. Kumar, "Indian Currency Note Denomination Recognition in Colour Images", International Journal on Advanced Computer Engineering and Communication Technology, Vol.1, No.1, ISSN 2278-5140, 2012.
- W. Kavinda, and S. Dhammika, "Bank notes recognition device for Sri Lankan vision impaired community", 8th International Conference Computer Science & Education (ICCSE), Colombo, 609-612, April 2013.
- N. Arora, N. Dhillon and K. Sharma, "Bank Automation System for Indian Currency a Graphical Approach", International Journal of Computer Science and Communication Engineering Special Issue on Recent Advances in Engineering & Technology NCRAET, ISSN 2319-7080, 2013. [10] X. Zhu, and M. Ren, "A Recognition Method of RMB Numbers Based on Character Features", 2nd International conference on Information, Electronics and Computer, May 2014. [11] D. Liliana, and I. Neforawati, "Automatic Recognition of Rupiah Currency using Naïve Bayes Classifier", Seminar on Electrical, Informatics and it Education, 2013. [12] L. Abu Doush and Sahar AL-Btoush "Currency recognition using a smartphone: Comparison between color SIFT and gray scale SIFT algorithms", Journal of King Saud University – Computer and Information Sciences, June 2016.