Abstract
We present a framework for accurate and robust extraction of parametric models of different types. The framework consists of four components: exploration, selection, fit, and a final selection. The exploration is a dynamic data-driven masking technique that proposes a set of models from which the selection chooses the ones that explain the data with minimal description length. Selection is performed by tabu search, a discrete optimization technique that outperforms annealing techniques on many classical optimization problems. Our robust fitting technique, which increases the accuracy of the selected models, may change the classification of data elements which requires the final selection. We apply our framework to simultaneously extract different geometric models from 2D and 3D data.
A. L. acknowledges the support provided by the Austrian National Fonds zur Förderung der wissenschaftlichen Forschung under grant S7002MAT and by the Ministry for Science and Technology of The Republic of Slovenia (Project J2-6187).
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Stricker, M., Leonardis, A. (1995). ExSel++: A general framework to extract parametric models. In: Hlaváč, V., Šára, R. (eds) Computer Analysis of Images and Patterns. CAIP 1995. Lecture Notes in Computer Science, vol 970. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60268-2_284
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DOI: https://doi.org/10.1007/3-540-60268-2_284
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