A Nonparametric Method for Fitting a Straight Line to a Noisy Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
What's in a Set of Points? (Straight Line Fitting)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parametric Model Fitting: From Inlier Characterization to Outlier Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Line Fitting in a Noisy Image by the Method of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
Piecewise approximation of contours through scale-space selection of dominant points
IEEE Transactions on Image Processing
Short Communication: A rectilinear Gaussian model for estimating straight-line parameters
Journal of Visual Communication and Image Representation
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The conventional least-squared-distance method of fitting a line to a set of data points is unreliable when the amount of random noise in the input (such as an image) is significant compared with the amount of data correlated to the line itself. Points which are far away from the line (outliers) are usually just noise, but they contribute the most to the distance averaging, skewing the line from its correct position. The author presents a statistical method of separating the data of interest from random noise, using a maximum-likelihood principle.