Name
Abstract | ||
|---|---|---|
We present a fully statistical framework for detecting pre-determined shape classes in 2D clouds of primitives (points, edges, and arcs), which are in turn extracted from images. An important goal is to provide a likelihood, and thus a confidence, of finding a shape class in a given data. This requires a model-based approach. We use a composite Poisson process: 1D Poisson process for primitives belonging to shapes and a 2D Poisson process for primitives belonging to clutter. An additive Gaussian model is assumed for noise in shape primitives. Combining these with a past stochastic model on shapes of continuous 2D contours, and optimization over unknown pose and scale, we develop a generalized likelihood ratio test for shape detection. We demonstrate the efficiency of this method and its robustness to clutter using both simulated and real data. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/CVPRW.2010.5543730 | CVPR Workshops |
Field | DocType | Volume |
Active shape model,Computer vision,Data modeling,Pattern recognition,Likelihood-ratio test,Computer science,Clutter,Image processing,Robustness (computer science),Gaussian network model,Gaussian process,Artificial intelligence | Conference | 2010 |
Issue | ISSN | Citations |
1 | 2160-7508 | 0 |
PageRank | References | Authors |
0.34 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing-yong Su | 1 | 156 | 10.93 |
| Zhiqiang Zhu | 2 | 1 | 2.05 |
| Anuj Srivastava | 3 | 2853 | 199.47 |
| Fred Huffer | 4 | 9 | 2.95 |