Abstract
A Robust Approach to Estimation of Parametric Models. This article presents a robust method for estimation of parametric models. The method consists of two procedures: model-recovery and model-selection. The model-recovery procedure systematically recovers a redundant set of parametric models in a local-to-global fashion, iteratively combining data classification and parameter estimation. The model-selection procedure, defined as a quadratic Boolean problem, then searches for the subset of the recovered models which produce the simplest global description. To achieve a computationally efficient method the model-recovery and the model-selection are combined in an iterative way. The main features of the method are a high degree of resistance to outliers and the insensitivity to incorrect initial estimates. The method has been successfully applied to linear as well as nonlinear parameter estimation problems, e.g. for recovering variable-order bivariate polynomials and superquadric models in range images, and parametric curve models in edge images.
This work was supported in part by The Ministry for Science and Technology of The Republic of Slovenia (Projects P2-1122 and J2-6187).
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Leonardis, A. (1996). A Robust Approach to Estimation of Parametric Models. In: Kropatsch, W., Klette, R., Solina, F., Albrecht, R. (eds) Theoretical Foundations of Computer Vision. Computing Supplement, vol 11. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6586-7_7
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DOI: https://doi.org/10.1007/978-3-7091-6586-7_7
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