A lightweight coin detection and classification algorithm for low quality images

DOI

10.2991/amcce-17.2017.105How to use a DOI?

Keywords

Coin Detection and Classification; Low Quality Image; Pattern Recognition

Abstract

Coin detection and classification system is important in banks. When coin images are captured by low end line-scan camera with poor illumination, their quality will be relatively low. A lightweight algorithm is proposed for this kind of images. Firstly, a modified Canny operator is designed to filter vertical line segments to speed up the ellipse fitting steps. Secondly, a rotation invariant feature is applied to describe the feature of a coin. Initial experiments show that the classification accuracy of the proposed algorithm is about 92.6% for the data given.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhi Liu
AU  - Yanru Sun
PY  - 2017/03
DA  - 2017/03
TI  - A lightweight coin detection and classification algorithm for low quality images
BT  - Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2017)
PB  - Atlantis Press
SP  - 602
EP  - 606
SN  - 2352-5401
UR  - https://doi.org/10.2991/amcce-17.2017.105
DO  - 10.2991/amcce-17.2017.105
ID  - Liu2017/03
ER  -