Identification of ancient coins based on fusion of shape and local features

Authors:
Reinhold Huber-Mörk;Sebastian Zambanini;Maia Zaharieva;Martin Kampel
Affiliations:
Austrian Institute of Technology GmbH, Business Unit High Performance Image Processing, Department Safety & Security, 2444, Seibersdorf, Austria;Vienna University of Technology, Computer Vision Lab, Institute of Computer-Aided Automation, Favoritenstr. 9/183-2, 1040, Vienna, Austria;Vienna University of Technology, Interactive Media Systems Group, Institute of Software Technology and Interactive Systems, Favoritenstr. 9-11/188/2, 1040, Vienna, Austria;Vienna University of Technology, Computer Vision Lab, Institute of Computer-Aided Automation, Favoritenstr. 9/183-2, 1040, Vienna, Austria
Venue:
Machine Vision and Applications
Year:
2011

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Abstract

We present a vision-based approach to ancient coins’ identification. The approach is a two-stage procedure. In the first stage an invariant shape description of the coin edge is computed and matching based on shape is performed. The second stage uses preselection by the first stage in order to refine the matching using local descriptors. Results for different descriptors and coin sides are combined using naive Bayesian fusion. Identification rates on a comprehensive data set of 2400 images of ancient coins are on the order of magnitude of 99%.