Name
Abstract | ||
|---|---|---|
In order to prevent more effectively the occurrence of the distortion of target weights’ distribution and further reduce system errors, a comprehensive improvement has been conducted on the algorithm’s weight updating and weight normalization to avoid the defects of the traditional AdaBoost image detection algorithm. It is proved that the improved algorithm is more effective. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/MVHI.2010.104 | MVHI |
Keywords | Field | DocType |
boosting,distributed computing,computer vision,learning artificial intelligence,degradation,face detection,machine vision,image processing,weight distribution | Normalization (statistics),Machine vision,Computer science,Image processing,Artificial intelligence,Face detection,Distortion,Computer vision,Object detection,AdaBoost,Pattern recognition,Boosting (machine learning),Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4244-6596-5 | 0 | 0.34 |
References | Authors | |
2 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peng Wu | 1 | 41 | 13.09 |
| Qu, Shenming | 2 | 0 | 0.34 |