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Optimal estimation in the computer vision context refers to estimating the parameters that describe the underlying problem from noisy observation. The estimation is done according to a given criterion of optimality, for which maximum likelihood is widely accepted. If Gaussian noise is assumed, it reduces to minimizing the Mahalanobis distance. If furthermore the Gaussian noise has a homogeneous and isotropic distribution, the procedure reduces to minimizing what is called the reprojection error.
One of the central tasks of computer vision is the extraction of 2D/3D geometric information from noisy image data. Here, the term image data refers to values extracted from images by image processing operations such as edge filters and interest point detectors. Image data are said to be noisyin the sense that...
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