Name
Abstract | ||
|---|---|---|
In this paper, a new General Kernel Density Estimator (GKDE) based robust estimator is presented. The GKDE based robust estimator utilizes GKDE to estimate the distribution of data points and by using local adaptive bandwidth estimator, the scale of inliers or user-specified error threshold is not need. Compared to ASKC, pbM and other Kernel Density Estimation based robust estimator which do not have locality, GKDE has higher resolution for inliers, and experiments show that it has higher precision than traditional robust estimator such as RANSAC, LMeds. We also applied GKDE based estimator to image mosaic for homography estimation. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-31919-8_15 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
new general kernel density,robust estimator,generalized kernel density estimation,homography estimation,higher precision,local adaptive bandwidth estimator,data point,higher resolution,image mosaic,kernel density estimation,user-specified error threshold,robust regression | Efficient estimator,Minimum-variance unbiased estimator,Stein's unbiased risk estimate,Pattern recognition,Bias of an estimator,Artificial intelligence,Trimmed estimator,Invariant estimator,Mathematics,Consistent estimator,Estimator | Conference |
Volume | Issue | ISSN |
7202 LNCS | null | 16113349 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhen Zhang | 1 | 53 | 6.68 |
| Yanning Zhang | 2 | 1613 | 176.32 |
| Rui Yao | 3 | 0 | 0.34 |
| Haisen Li | 4 | 49 | 5.47 |
| Yu Zhu | 5 | 88 | 12.65 |