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Other researchers tried to cope with difficult situations where the noise scale is not known and/or multiple model instances are present. The first problem has been tackled in the work by Wang and Suter.<ref>H. Wang and D. Suter, [https://ieeexplore.ieee.org/abstract/document/1335451/ Robust adaptive-scale parametric model estimation for computer vision]., IEEE Transactions on Pattern Analysis and Machine Intelligence 26 (2004), no. 11, 1459–1474</ref> Toldo et al. represent each datum with the characteristic function of the set of random models that fit the point. Then multiple models are revealed as clusters which group the points supporting the same model. The clustering algorithm, called J-linkage, does not require prior specification of the number of models, nor does it necessitate manual parameters tuning.<ref>R. Toldo and A. Fusiello, [https://pdfs.semanticscholar.org/0455/e5596d734e3dcf60c0179efb6404e62ceabb.pdf Robust multiple structures estimation with J-linkage], European Conference on Computer Vision (Marseille, France), October 2008, pp. 537–547.</ref>
RANSAC has also been tailored for recursive state estimation applications, where the input measurements are corrupted by outliers and [[Kalman filter]] approaches, which rely on a [[Normal distribution|Gaussian distribution]] of the measurement error, are doomed to fail. Such an approach is dubbed KALMANSAC.<ref>A. Vedaldi, H. Jin, P. Favaro, and S. Soatto, [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.184.812&rep=rep1&type=pdf KALMANSAC: Robust filtering by consensus], Proceedings of the International Conference on Computer Vision (ICCV), vol. 1, 2005, pp. 633–640</ref>
==Related methods==
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