A lightweight coin detection and classification algorithm for low quality images
- 10.2991/amcce-17.2017.105How to use a DOI?
- Coin Detection and Classification; Low Quality Image; Pattern Recognition
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.
- © 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/).
Cite this article
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 -