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Tag Archives: nonlinear regression
Vanishing points in presence of noise
Most selfcalibration algorithms require a prior knowledge of the camera calibration matrix ; as an instance, you need it to normalize the image points as and therefore fit the essential matrix . With most commercial cameras it is safe to … Continue reading
Posted in Uncategorized
Tagged Alciatore and Miranda, calibration matrix, computer vision, constrained minimum, essential matrix, fitting, focal length, image of the absolute conic, Lagrange multipliers, lagrangian, least squares, noise, nonlinear regression, outlier, pixel pitch, principal point, RANSAC, vanishing point
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Orthogonal least squares fitting of a sphere/2
As said in my previous post, to obtain an orthogonal least squares fitting of a sphere to a cloud of points one should minimize the function Dave Eberly calls this the energy function, probably as a metaphorical reference to the … Continue reading
Orthogonal least squares fitting of a sphere
Recently I presented a linear method to obtain centre and radius of a sphere given four or more points on its surface, not all beginning to the same plane. This method is not completely satisfactory when working with more than … Continue reading