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The distribution of many statistics can be heavily influenced by outliers, values that are 'way outside' the bulk of the data. A typical strategy to account for, without eliminating altogether, these outlier values is to 'reset' outliers to a specified percentile (or an upper and lower percentile) of the data. For example, a 90% winsorization ...
In this work a statistical method based on the distance distribution is used to deal with outliers, occlusion, appearance, and disappearance, which enables subset-subset matching. There exist many ICP variants, [6] from which point-to-point and point-to-plane are the most popular. The latter usually performs better in structured environments ...
A simple example is fitting a line in two dimensions to a set of observations. Assuming that this set contains both inliers, i.e., points which approximately can be fitted to a line, and outliers, points which cannot be fitted to this line, a simple least squares method for line fitting will generally produce a line with a bad fit to the data including inliers and outliers.
RANSAC is also commonly used with a PnP method to make the solution robust to outliers in the set of point correspondences. P3P methods assume that the data is noise free, most PnP methods assume Gaussian noise on the inlier set.
The book has seven chapters. [1] [4] The first is introductory; it describes simple linear regression (in which there is only one independent variable), discusses the possibility of outliers that corrupt either the dependent or the independent variable, provides examples in which outliers produce misleading results, defines the breakdown point, and briefly introduces several methods for robust ...
The Theil–Sen estimator of a set of sample points with outliers (black line) compared to the non-robust ordinary least squares line for the same set (blue). The dashed green line represents the ground truth from which the samples were generated.
The modified Thompson Tau test is used to find one outlier at a time (largest value of δ is removed if it is an outlier). Meaning, if a data point is found to be an outlier, it is removed from the data set and the test is applied again with a new average and rejection region. This process is continued until no outliers remain in a data set.
The observations that are not marked as outliers are surrounded by a loop, the convex hull of the observations within the fence. [ 6 ] An asterisk symbol (*) near the center of the graph is used to mark the depth median, the point with the highest possible Tukey depth.