Abstract
This paper tackles the problem of fitting multiple instances of a model to data corrupted by noise and outliers. The proposed solution is based on random sampling and conceptual data representation. Each point is represented with the characteristic function of the set of random models that fit the point. A tailored agglomerative clustering, called J-linkage, is used to group points belonging to the same model. The method does not require prior specification of the number of models, nor it necessitate parameters tuning. Experimental results demonstrate the superior performances of the algorithm.
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Toldo, R., Fusiello, A. (2008). Robust Multiple Structures Estimation with J-Linkage. In: Forsyth, D., Torr, P., Zisserman, A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5302. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88682-2_41
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DOI: https://doi.org/10.1007/978-3-540-88682-2_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88681-5
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