Invariant Features Extraction for Banknote Classification
Peng Wang1, Peng Liu
1Heilongjiang Institute of Science and Technology
Available Online December 2008.
- 10.2991/jcis.2008.46How to use a DOI?
- banknote classification; invariant feature; neural networks; image processing; hardware plane
An invariant feature extraction method is proposed for banknote classification. The movement of banknote is complex in the channel of financial instruments. The scale is various. The rotation and translation are also to occur. The method of feature extraction is insensitive to the variety of scale, rotation and translation. It decreases the data variety and improves the reliability of banknote classification. Furthermore, the computation complexity is low in order to meet to the requirement of real-time banknote image processing and classification. The invariant feature extraction method has performed very well when they are applied in banknote sorters.
- © 2008, 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 - Peng Wang AU - Peng Liu PY - 2008/12 DA - 2008/12 TI - Invariant Features Extraction for Banknote Classification BT - Proceedings of the 11th Joint Conference on Information Sciences (JCIS 2008) PB - Atlantis Press SP - 270 EP - 277 SN - 1951-6851 UR - https://doi.org/10.2991/jcis.2008.46 DO - 10.2991/jcis.2008.46 ID - Wang2008/12 ER -