What is it about?
The corrupted data pose difficulty in accurate estimation of underlying geometric model parameters. For this, a new algorithm has been proposed to efficiently and accurately estimate the model parameters in heavily corrupted data points.
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Why is it important?
The proposed method is implemented over a wide range of data points. It is a robust technique and observed to outperform the widely used RANSAC algorithm in terms of accuracy and computational efficiency.
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This page is a summary of: A Clustering and Outlier Detection Scheme for Robust Parametric Model Estimation for Plane Fitting, Applied Mechanics and Materials, September 2015, Trans Tech Publications,
DOI: 10.4028/www.scientific.net/amm.789-790.770.
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