ASSC: A new robust estimator for data with multiple structures
Date
2003
Authors
Wang, Hanzi
Suter, David
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Report
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Hanzi Wang and David Suter
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Abstract
Estimating information from data with multiple structures has obtained more and more attention
in computer vision community. When data include multiple structures, two major steps should be
taken: i) robustly estimate the parameters of a model, and ii) differentiate inliers from outliers. In
this paper, we propose two new robust techniques — robust Two-Step Scale estimator (TSSE) and
robust Adaptive Scale Sample Consensus (ASSC) estimator. The first estimator (TSSE) applies
nonparametric density estimation and density gradient estimation techniques, to robustly estimate
the scale of inliers for heavily contaminated data. The second estimator (ASSC) is a complete
robust fitting estimator. ASSC is based on both Random Sample Consensus (RANSAC) and TSSE.
The ASSC estimator can tolerate more than 80% outliers. The main advantage of the ASSC
estimator over RANSAC is that prior knowledge about the scale of inliers is not needed. The ASSC
estimator can simultaneously estimate the parameters of a model and the scale of inliers
belonging to that model. Comparative experiments show that the ASSC estimator has better
robustness to heavily corrupted data with multiple structures than other robust methods: such as
Least Median Squares (LMedS), Residual Consensus (RESC), and Adaptive Least Kth order
Squares (ALKS).
School/Discipline
School of Computer Science