Gabor wavelet based automatic coin classsification

Authors:
Taraggy M. Ghanem;Mohamed N. Moustafa;Hussein I. Shahein
Affiliations:
Computer and Systems Department, Faculty of Engineering, Ain Shams University, Cairo, Egypt and Faculty of Computer Science, Misr International University, Cairo, Egypt;Computer and Systems Department, Faculty of Engineering, Ain Shams University, Cairo, Egypt;Computer and Systems Department, Faculty of Engineering, Ain Shams University, Cairo, Egypt
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 4
Cited 0

Image Representation Using 2D Gabor Wavelets

IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing

Digital Image Processing
Classification of coins using an eigenspace approach

Pattern Recognition Letters
A fast and reliable coin recognition system

Proceedings of the 29th DAGM conference on Pattern recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present an automatic coin classifier mainly depending on visual features. Our multistage system starts out by segmentation using circular Hough Transform, features extraction by two complementary cues and finally classification by simple nearest neighbor measure. Our features extraction process relies on rotation invariant edge orientation followed by Gabor wavelet convolution. Testing on the publicly available portion of a benchmark European coins database, we can correctly classify 93.5% and 98% of the coins using single face and double faces images respectively. We also show that our correct classification rate can reach 99.8% when adding the coin thickness measurement (which is available for this database).