Name
Abstract | ||
|---|---|---|
This paper presents Local Rank Patterns (LRP) - novel features for rapid object detection in images which are based on existing features Local Rank Differences (LRD). The performance of the novel features is thoroughly tested on frontal face detection task and it is compared to the performance of the LRD and the traditionally used Haar-like features. The results show that the LRP surpass the LRD and the Haar-like features in the precision of detection and also in the average number of features needed for classification. Considering recent successful and efficient implementations of LRD on CPU, GPU and FPGA, the results suggest that LRP are good choice for object detection and that they could replace the Haar-like features in some applications in the future. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-642-02345-3_24 | ICCVG |
Keywords | Field | DocType |
haar-like feature,rapid object detection,novel feature,efficient implementation,object detection,frontal face detection task,novel features,average number,local rank patterns,local rank differences,good choice,face detection | Computer vision,Object detection,AdaBoost,Pattern recognition,Computer science,Field-programmable gate array,Implementation,Artificial intelligence,Face detection | Conference |
Volume | ISSN | Citations |
5337 | 0302-9743 | 13 |
PageRank | References | Authors |
0.91 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michal Hradis | 1 | 132 | 14.19 |
| Adam Herout | 2 | 248 | 35.39 |
| Pavel Zemcik | 3 | 66 | 7.58 |