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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Stewart, C.V. |
| Copyright Year | 1994 |
| Description | Author affiliation: Dept. of Comput. Sci., Rensselaer Polytech. Inst., Troy, NY, USA (Stewart, C.V.) |
| Abstract | MINPRAN, a new robust operator, finds good fits in data sets where more than 50% of the points are outliers. Unlike other techniques that handle large outlier percentages, MINPRAN does not rely on a known error bound for the good data. Instead it assumes that the bad data are randomly (uniformly) distributed within the dynamic range of the sensor. Based on this, MINPRAN uses random sampling to search for the fit and the number of inliers to the fit that are least likely to have occurred randomly. It runs in time O(N/sup 2/+SNlogN), where S is the number of random samples and N is the number of data points. We demonstrate analytically and experimentally that MINPRAN distinguishes good fits from fits to random data, and that MINPRAN finds accurate fits and nearly the correct number of inliers, regardless of the percentage of true inliers.< |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 718712 |
| Page Count | 8 |
| File Format | |
| ISBN | 0818658258 |
| ISSN | 10636919 |
| DOI | 10.1109/CVPR.1994.323951 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1994-06-21 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Machine vision Statistics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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