Name
Abstract | ||
|---|---|---|
We present a novel statistical framework for detecting pre-determined shape classes in 2D cluttered point clouds, which are in turn extracted from images. In this model based approach, we use a 1D Poisson process for sampling points on shapes, a 2D Poisson process for points from background clutter, and an additive Gaussian model for noise. 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/ICPR.2010.647 | ICPR |
Keywords | Field | DocType |
cluttered point cloud,shape detection,pre-determined shape class,novel statistical framework,background clutter,past stochastic model,poisson process,generalized likelihood ratio test,additive gaussian model,maximum likelihood estimation,shape,point cloud,gaussian model,object recognition,data models,stochastic model,computational modeling,noise,clutter,stochastic processes | Computer vision,Data modeling,Likelihood-ratio test,Pattern recognition,Clutter,Computer science,Stochastic process,Robustness (computer science),Artificial intelligence,Stochastic modelling,Gaussian network model,Point cloud | Conference |
Citations | PageRank | References |
1 | 0.36 | 4 |
Authors | ||
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 |