An empirical approach for currency identification
| aut.relation.endpage | 13 | |
| aut.relation.pages | 13 | |
| aut.relation.startpage | 1 | |
| aut.researcher | Yan, Wei-Qi | |
| dc.contributor.author | Yan, W-Q | |
| dc.contributor.author | Chambers, J | |
| dc.contributor.author | Garhwal, A | |
| dc.date.accessioned | 2013-12-31T04:00:48Z | |
| dc.date.accessioned | 2014-01-24T07:29:38Z | |
| dc.date.available | 2013-12-31T04:00:48Z | |
| dc.date.available | 2014-01-24T07:29:38Z | |
| dc.date.copyright | 2013-12-31 | |
| dc.date.issued | 2013-12-31 | |
| dc.description.abstract | Currency identification is the application of systematic methods to determine authenticity of questioned currency. However, identification analysis is a difficult task requiring specially trained examiners, the most important challenge is automating the analysis process reducing human labor and time. In this study, an empirical approach for automated currency identification is formulated and a prototype is developed. A two parts feature vector is defined comprised of color features and texture features. Finally the banknote in question is classified by a Feedforward Neural Network (FNN) and a measurement of the similarity between existing samples and suspect banknote is output. | |
| dc.identifier.citation | Multimedia Tools and Applications, pp.1 - 13 (13), doi: 10.1007/s11042-013-1833-x | |
| dc.identifier.doi | 10.1007/s11042-013-1833-x | |
| dc.identifier.uri | https://hdl.handle.net/10292/6568 | |
| dc.language | English | |
| dc.publisher | Springer | |
| dc.relation.replaces | http://hdl.handle.net/10292/6352 | |
| dc.relation.replaces | 10292/6352 | |
| dc.relation.uri | http://dx.doi.org/10.1007/s11042-013-1833-x | |
| dc.rights | An author may self-archive an author-created version of his/her article on his/her own website and or in his/her institutional repository. He/she may also deposit this version on his/her funder’s or funder’s designated repository at the funder’s request or as a result of a legal obligation, provided it is not made publicly available until 12 months after official publication. He/ she may not use the publisher's PDF version, which is posted on www.springerlink.com, for the purpose of self-archiving or deposit. Furthermore, the author may only post his/her version provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at www.springerlink.com”. (Please also see Publisher’s Version and Citation). | |
| dc.rights.accessrights | OpenAccess | |
| dc.subject | Currency identification | |
| dc.subject | Neural network | |
| dc.subject | Empirical approach | |
| dc.title | An empirical approach for currency identification | |
| dc.type | Journal Article | |
| pubs.elements-id | 159778 | |
| pubs.organisational-data | /AUT | |
| pubs.organisational-data | /AUT/Design & Creative Technologies | |
| pubs.organisational-data | /AUT/Design & Creative Technologies/School of Computing & Mathematical Science |