Name
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. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ICITA.2005.176 | ICITA (1) |
Keywords | Field | DocType |
mean shift,number plate,mahalanobis classifier,number plate region,high robustness,aspect ratio,number plate detection,accurate number plate detection,mean shift segmentation,edge density,candidate region,feature extraction,image recognition,robustness,helium,kernel,statistical analysis,information technology,image segmentation | Kernel (linear algebra),Mean shift segmentation,Pattern recognition,Computer science,Feature extraction,Robustness (computer science),Image segmentation,Mahalanobis distance,Artificial intelligence,Mean-shift,Classifier (linguistics) | Conference |
ISBN | Citations | PageRank |
0-7695-2316-1 | 4 | 0.72 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenjing Jia | 1 | 325 | 45.08 |
| Huaifeng Zhang | 2 | 240 | 18.84 |
| Xiangjian He | 3 | 932 | 132.03 |