Abstract
Given n points in 3D, sampled from k original planes (with sampling errors), a new probabilistic method for detecting coplanar subsets of points in O(k6) steps is introduced. The planes are reconstructed with small probability of error. The algorithm reduces the problem of reconstruction to the problem of clustering in R3 and thereby produces effective results. The algorithm is significantly faster than other known algorithms in most cases.
Original language | English |
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Pages (from-to) | 153-166 |
Number of pages | 14 |
Journal | Advances in Computational Mathematics |
Volume | 17 |
Issue number | 1-2 |
DOIs | |
State | Published - 2002 |
Keywords
- Clustering
- Distance
- Hyperplanes
- Measurement
- Probabilistic algorithm
- Robust-metric