Name
Abstract | ||
|---|---|---|
In this paper, we propose a method for robustly estimating camera homography using fuzzy RANSAC from the correspondences between consecutive two images. We use a fuzzified version of the original RANSAC algorithm to obtain accurate camera homography in the presence of outliers. The drawback of RANSAC is that its performance depends on a prior knowledge of the outlier scale. To resolve this problem, the proposed method classifies all samples into three classes (good sample set, bad sample set and vague sample set) using fuzzy classification. It then improves classification accuracy omitting outliers by iteratively sampling in only good sample set. Experimental results show the robustness of the proposed approach for computing a homography on real image sequence. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74472-6_81 | ICCSA (1) |
Keywords | Field | DocType |
good sample set,fuzzy ransac,vague sample set,fuzzy classification,accurate camera homography,classification accuracy,original ransac algorithm,bad sample set,robust estimation,camera homography,ransac,homography,robust estimator,outlier | Computer vision,Pattern recognition,Fuzzy classification,Computer science,RANSAC,Fuzzy logic,Outlier,Robustness (computer science),Homography,Artificial intelligence,Sampling (statistics),Real image | Conference |
Volume | ISSN | ISBN |
4705 | 0302-9743 | 3-540-74468-1 |
Citations | PageRank | References |
12 | 1.06 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joong-Jae Lee | 1 | 48 | 6.89 |
| Gye-Young Kim | 2 | 116 | 24.67 |