Name
Abstract | ||
|---|---|---|
Conventional contour tracking algorithms with level set often use generative models to construct the energy function. For tracking through cluttered and noisy background, however, a generative model may not be discriminative enough. In this paper we integrate the discriminative methods into a level set framework when constructing the level set energy function. We train a set of weak classifiers to distinguish the object from the background. Each weak classifier is designed to select the most discriminative feature space and integrated via AdaBoost according to their training errors. We also introduce a novel interaction term to explore the correlation between pixels near the object edge. This term together with the discriminative model both enhance the discriminative power of the level set. The experimental results show that the contour tracked by our approach is more accurate than the conventional algorithms with the generative model. Our algorithm successfully tracks the object contour even in a cluttered environment. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICPR.2010.429 | ICPR |
Keywords | Field | DocType |
level set framework,discriminative method,energy function,level set,cluttered background,discriminative enough,discriminative power,pattern classification,contour tracking,set theory,level set energy function,generative model,adaboost,weak classifier,training errors,discriminative feature space,edge detection,tracking,classifiers,noisy background,discriminative level set,discriminative model,pixel,noise measurement,color,feature space,computational modeling,shape | Set theory,Computer vision,Feature vector,AdaBoost,Pattern recognition,Edge detection,Computer science,Level set,Artificial intelligence,Classifier (linguistics),Discriminative model,Generative model | Conference |
ISSN | ISBN | Citations |
1051-4651 | 978-1-4244-7542-1 | 4 |
PageRank | References | Authors |
0.41 | 9 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Li | 1 | 54 | 6.00 |
| Xiaoqin Zhang | 2 | 952 | 72.31 |
| Jun Gao | 3 | 8 | 0.85 |
| Weiming Hu | 4 | 336 | 19.80 |
| Haibin Ling | 5 | 4531 | 215.76 |
| Xue Zhou | 6 | 194 | 11.81 |