Coin detection and recognition using neural networks

Author

Roomi, S. Mohamed Mansoor ; Jayanthi Rajee, R.B.

fYear

2015

fDate

19-20 March 2015

Firstpage

1

Lastpage

6

Abstract

Coin identification and recognition and is important to enhance the extended operation of Vending machines, Pay phone system and coin counting machines. Coin recognition is a difficult task in machine intelligence and computer vision problems because of its various rotations and widely changed patterns. Therefore, an efficient algorithm is designed to be robust and invariant to rotation, translation and scaling. The objective of this work is to find whether the object is coin or not if so denomination of the coin is found. The Fourier approximation of the coin image is used to reduce the variations on surface of coin such as light reflection effect. Then coins can be distinguished by feeding those features into a multi-layered BP neural network.

Keywords

backpropagation; computer vision; financial data processing; image recognition; multilayer perceptrons; vending machines; Fourier approximation; coin counting machines; coin detection; coin identification; coin recognition; computer vision problems; machine intelligence; multilayered BP neural network; neural networks; pay phone system; vending machines; Biological neural networks; Computers; Feature extraction; Fourier transforms; Image edge detection; Image segmentation; BP neural network; Fourier transform; coordinate transform; object recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuit, Power and Computing Technologies (ICCPCT), 2015 International Conference on

Conference_Location

Nagercoil

Type

conf

DOI

10.1109/ICCPCT.2015.7159434

Filename

7159434