Outlier Detection for Robust Parameter Estimation Against Multi-modeled/structured Data

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Model parameter estimation and automatic outlier detection is a fundamental and important problem in computer vision. Vision data is noisy and usually contains multiple structures, models of interest. RANSAC has been proven to be the most popular and effective solution for such problem, but it requires some user-defined threshold to discriminate inliers/outliers. It is then improved by the adaptive-scale robust estimators, which do not require the user-defined threshold and detect inliers automatically. However, there still remains some problem. The problem is that these adaptive-scale robust estimators do not focus on the accurate inlier detection. In this paper, we propose several adaptive-scale robust estimators which can detect inliers accurately. There are two reasons for the idea of accurate inlier detection. First, if a robust estimator detects inliers better, then the robustness of the estimation can be improved. Second, in many real applications such as motion segmentation and range image segmentation, if the inlier detection is not very well, then a structure can be broken into smaller structures, an under-segmentation problem, or united with the other structures, an oversegmentation problem. In the experiments, various analytic simulations in many aspects have shown the advantage of the proposed robust estimators compared to several latest robust estimators. The real experiments were also performed to prove the validation of the proposed estimators in real applications.
2010-05-20

Outlier Detection for Robust Parameter Estimation Against Multi-modeled/structured Data

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