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Numismatic Image Segmentation: An Empirical Study

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Anindya Halder, Amit Kumar Upadhyay, Sujit Kumar Das

Anindya Halder, Amit Kumar Upadhyay and Sujit Kumar Das. Numismatic Image Segmentation: An Empirical Study. International Journal of Computer Applications 160(4):36-39, February 2017. BibTeX

	author = {Anindya Halder and Amit Kumar Upadhyay and Sujit Kumar Das},
	title = {Numismatic Image Segmentation: An Empirical Study},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2017},
	volume = {160},
	number = {4},
	month = {Feb},
	year = {2017},
	issn = {0975-8887},
	pages = {36-39},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2017913044},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Numismatic coins are one of the important elements to know history of economic, culture and society in ancient as well as present time. Extraction of information from numismatic coins in traditional manual method sometimes contains human error and in the same time it is time consuming. Engineering in this aspects deal with problems of traditional method. Engineering tasks namely pattern recognition, computer vision, and object authentication are high level computational tasks where machine deal with image data for processing. To do these high level tasks image segmentation is a basic process. Its purpose is to separate the targets from the background in an image in order to provide the basis for the subsequent sorting, recognition and indexing. Again segmentation methods depends on types of image data, we are dealing with. In this paper we discussed some existing methods of image segmentation and tried to find out suitable method for coin images. Maximum Entropy Thresholding(MET) based on normalised histogram method giving us more suitable segmented image.


  1. N¨olle, M., Penz, H., Rubik, M., Mayer, K., Holl¨ander, I., Granec, R.: Dagobert-a new coin recognition and sorting system. In: Proceedings of the 7th International Conference on Digital Image Computing-Techniques and Applications (2003). .
  2. Van Der Maaten, L.J., Poon, P.: Coin-o-matic: A fast system for reliable coin classification. In: Proc. of the Muscle CIS Coin Competition Workshop, pp. 7–18 (2006).
  3. Reisert, M., Ronneberger, O., Burkhardt, H.: A fast and reliable coin recognition system. In: Hamprecht, F.A., Schn¨orr, C., J¨ahne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 415–424. Springer, Heidelberg (2007).
  4. Zambanini, S., Kampel, M.: Robust automatic segmentation of ancient coins. In: Proc. Conf. on Comp. Vision Theory and Appl., vol. 2, pp. 273–276 (2009).
  5. Image Segmentation Techniques Rajeshwar Dass, priyanka, Swapna Devi IJECT Vol. 3, Issue 1, Jan. –March(2012).
  6. Segmentation Techniques For Image Analysis IJAERS/Vol. I/ Issue II/January-March, 2012.
  7. P.Daniel Ratna Raju and G.Neelima “Image Segmentation by using Histogram Thresholding” IJCSET January 2012 Vol 2, Issue 1,776-779.


Numismatic data, Hough Transformation, Maximum Entropy Thresholding