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EnglishThis paper deals with the extraction of multiple models from noisy or outlier-contaminated data. We cast the multi-model fitting problem in terms of set coverage, deriving a simple and effective method that generalizes Ransac to multiple models and deals with intersecting structures and outliers in a straightforward and principled manner, while avoiding the typical shortcomings of sequential approaches and those of clustering. The method compares favorably against the state-of-the-art on simulated and publicly available real data-sets.
| Titolo: | Multiple Models Fitting as a Set Coverage Problem |
| Autori: | |
| Data di pubblicazione: | 2016 |
| Abstract: | This paper deals with the extraction of multiple models from noisy or outlier-contaminated data. We cast the multi-model fitting problem in terms of set coverage, deriving a simple and effective method that generalizes Ransac to multiple models and deals with intersecting structures and outliers in a straightforward and principled manner, while avoiding the typical shortcomings of sequential approaches and those of clustering. The method compares favorably against the state-of-the-art on simulated and publicly available real data-sets. |
| Handle: | http://hdl.handle.net/11390/1097667 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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