Abstract
This paper presents a robust method for number plate detection, where mean shift segmentation is used to segment color vehicle images into candidate regions. Three features are extracted in order to decide whether a candidate region contains a number plate, namely, rectangularity, aspect ratio, and edge density. Then, the Mahalanobis classifier is used with respect to the above three features to detect number plate regions accurately. The experimental results show that our algorithm produces high robustness and accuracy.
| Original language | English |
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| Title of host publication | Proceedings - 3rd International Conference on Information Technology and Applications, ICITA 2005 |
| Pages | 732-737 |
| Number of pages | 6 |
| Publication status | Published - 2005 |
| Externally published | Yes |
| Event | 3rd International Conference on Information Technology and Applications, ICITA 2005 - Sydney, Australia Duration: 4 Jul 2005 → 7 Jul 2005 |
Publication series
| Name | Proceedings - 3rd International Conference on Information Technology and Applications, ICITA 2005 |
|---|---|
| Volume | I |
Conference
| Conference | 3rd International Conference on Information Technology and Applications, ICITA 2005 |
|---|---|
| Country/Territory | Australia |
| City | Sydney |
| Period | 4/07/05 → 7/07/05 |
ASJC Scopus subject areas
- General Engineering
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Jia, W., Zhang, H., & He, X. (2005). Mean shift for accurate number plate detection. In Proceedings - 3rd International Conference on Information Technology and Applications, ICITA 2005 (pp. 732-737). Article 1488896 (Proceedings - 3rd International Conference on Information Technology and Applications, ICITA 2005; Vol. I).