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Tag Archives: constrained minimum
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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Constrained homography
When matching two images, say L and R, of the same scene from different perspectives, sometimes it is useful to assume that the images are related by a homography, either because the observed object is planar, or because the images … Continue reading
Lagrangian rejection and fitting/2
Summarizing the previous article: we have a set of samples, mostly affected by errors having a Gaussian distribution. Zero or more of the samples may be outliers, that is affected by exceptional errors which do not fit in the Gaussian … Continue reading
Posted in Uncategorized
Tagged constrained minimum, fitting, lagrangian multipliers, Least Trimmed Squares, LMedS, model, outlier, RANSAC, rejection, sample, shape
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