Name
Abstract | ||
|---|---|---|
Multimodal distribution fitting is an important task in pattern recognition. For instance, the predetection which is the preliminary stage that limits image areas to be processed in the detection stage amounts to the modeling of a multimodal distribution. Different techniques are available for such modeling. We propose a pros and cons analysis of multimodal distribution fitting techniques convenient for object predetection in images. This analysis leads us to propose efficient and accurate variants over the previously proposed techniques as shown by our experiments. These variants are based on parametric distribution fitting in the RKHS space induced by a positive definite kernel. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICPR.2006.883 | ICPR |
Keywords | Field | DocType |
image recognition,object detection,RKHS space,image object predetection,kernel parametric distribution fitting,multimodal distribution fitting,pattern recognition | Kernel (linear algebra),Object detection,Computer vision,Pattern recognition,Computer science,Multimodal distribution,Parametric statistics,Artificial intelligence,Positive-definite kernel,Reproducing kernel Hilbert space | Conference |
Volume | ISSN | ISBN |
2 | 1051-4651 | 0-7695-2521-0 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jean-Philippe Tarel | 1 | 805 | 56.63 |
| Sabri Boughorbel | 2 | 127 | 15.32 |