Morphological based Segmentation and Recognition of Indian Coins
|International Journal of Electronics and Communication Engineering|
|© 2016 by SSRG - IJECE Journal|
|Volume 3 Issue 2|
|Year of Publication : 2016|
|Authors : P.Durga Devi and M.Chandrakala|
How to Cite?
P.Durga Devi and M.Chandrakala, "Morphological based Segmentation and Recognition of Indian Coins," SSRG International Journal of Electronics and Communication Engineering, vol. 3, no. 2, pp. 4-10, 2016. Crossref, https://doi.org/10.14445/23488549/IJECE-V3I2P102
Coins have very much importance in human’s day to day life, which are used in everyone’s daily routine like banks, super markets, vending machines etc. So, there is a basic need to automate the counting and sorting of coins. Coin recognition applications play an important role in industry and computer vision. Many approaches developed for the coin detection and calculate its corresponding values. This paper recognizes Indian coins of different denomination The recognition process consists of three steps, 1) we present a simple and fast method for coin segmentation, based on morphological thresholding technique to remove noise and to enhance the quality of coin image, 2) we applied some simple descriptors like mean intensity, area, perimeter to extract the regional features of the coins used for recognition and sorting and 3) we performed edge detection using statistical operators. Only after detecting the edges in the image the number can be recognized. In this paper we describe the pattern recognition method used for identification of coins in the new coin recognition and sorting system
Edge detection, features extraction, segmentation, thresholding, top-hat transformation.
 M. Reisert, O. Ronneberger, and H. Burkhardt, An efficient gradient based registration technique for coin recognition, In Proc. of the Muscle CIS Coin Competition, 2006, 19–31.
 L. J. Van der Maaten, and P. Poon, Coin-o-matic: A fast system for reliable coin classification, In Proc. of the Muscle CIS Coin Competition, 2006, 07–18.
 M. Nolle, H. Penz, M. Rubik, K. J. Mayer, I. Holl¨ander, and R. Granec, Dagobert – New coin recognition and sorting system, In Proc. of DICTA’03, 2003, 329–338.
 S. Zambanini, and M. Kampel, Robust automatic segmentation of ancient coins, Proc. Conf. on Comp. Vision Theory and Appl., 2009, Vol. 2, 273–276.
 M. Kampel, R. Huber-Mork, and M. Zaharieva, Imagebased retrieval and identification of ancient coins, IEEE Intell. Syst. 24(2), 2009, 26–34.
 Hafeez Anwar, Sebastian Zambanini, Martin Kampel, and Klaus Vondrovec , IEEE signal processing magazine, 32(4), 2015, 64-74.
 R. Bremananth, B. Balaji, M. Sankari, A. Chitra, “A New Approach to Coin Recognition using Neural Pattern Analysis”, 2005 annual IEEE, Indicon, 366-370.
 P. Harrop, New Electronics for payment, IEE review, 35(9), 1989, 339-342.
 M. Fukumi, S. Omatu, "Rotation-Invariant Neural Pattem Recognition System with Application to Coin Recognition", IEEE Trans. Neural Networks, 3(2), 1992, 272-279. 1992.
 Linlin Shen, Sen Jia, and Zhen Ji, “Statictics of Gabor Features for Coin Recognition”, IEEE International Workshop on Imaging Systems and Techniques, IST '09, 2009, 295-298.
 M.Fukumi, and S.Omatu,"Rotation-lnvariant Neural Pattem Recognition System Estimating a Rotation angle", IEEE Trans. Neural Networks,8(3), 1997, 568-581.
 K. Q. Sun, and N. Sang, Morphological enhancement of vascular angiogram with multiscale detected by Gabor filters, Electron Lett, 44(2), 2008.
 T. Sreenivasn, P.Saranya, S.R.M. Padmapriya, “ An efficient VLSI architecture based automatic vending machine using FSM”, International Conference on Futuristic Trends in Computing and Communication, 2015, 6-10.