Interferogram Analysis using Active Instance-based Learning

O. Fuentes and T. Solorio (Mexico)


active learning, instancebased learning, locally weighted regression


In this paper we present an efficient solution, based on machine learning, to the problem of obtaining the phase of a set of interferograms. The algorithm learns the function from an interferogram, given as a gray scale image, to a vector of aberration coefficients that form the Seidel aberration representation of the in terferogram. The algorithm uses principal component analysis to reduce the dimensionality of the task and applies a modified version to the locally-weighted re gression algorithm to find aberration coefficients. The method is faster than others previously presented in the literature, and is also noise-insensitive. We tested our algorithm using a large set of simulated interfero grams, and our experiments show very accurate results using both noiseless and noisy interferograms.

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