Name
Title | ||
|---|---|---|
Robust Point Matching Using Mixture Of Asymmetric Gaussians For Nonrigid Transformation |
Abstract | ||
|---|---|---|
In this paper, we present a novel robust method for point matching under noise, deformation, occlusion and outliers. We introduce a new probability model to represent point sets, namely asymmetric Gaussian (AG), which can capture spatially asymmetric distributions. Firstly, we use a mixture of AGs to represent the point set. Secondly, we use L-2-minimizing estimate (L2E), a robust estimator to estimate densities between two point sets, to estimate the transformation function in reproducing kernel Hilbert space (RKHS) with regularization theory. Thirdly, we use low-rank kernel matrix approximation to reduce the computational complexity. Experimental results show that our method outperforms the comparative state-of-the-art methods on most scenarios, and it is quite robust to noise, deformation, occlusion and outliers. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-16817-3_28 | COMPUTER VISION - ACCV 2014, PT IV |
Field | DocType | Volume |
Tikhonov regularization,Computer science,Robust statistics,Artificial intelligence,Point set registration,Mathematical optimization,Transformation (function),Pattern recognition,Algorithm,Outlier,Gaussian,Mixture model,Reproducing kernel Hilbert space | Conference | 9006 |
ISSN | Citations | PageRank |
0302-9743 | 11 | 0.48 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gang Wang | 1 | 11 | 0.48 |
| Zhicheng Wang | 2 | 176 | 17.00 |
| Weidong Zhao | 3 | 25 | 1.56 |
| Qiangqiang Zhou | 4 | 37 | 3.43 |