In this talk the engeineer Andrea Fusiello will tackle the problem of fitting multiple instances of a model to data corrupted by noise and outliers. A solution will be proposed based on random sampling and conceptual data representation. Each data point is represented with the characteristic function of the set of random models that fit that 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 necessitates parameter tuning. Finally, I will touch upon an application of this technique to unsupervised reconstruction of architectural models in terms of high-level primitives.