Abstract

Abstract : The research project has built a theoretical foundation for using the method of sieves to adapt classical estimation principles such as maximum likelihood and least squares to problems with infinite dimensional parameter spaces. The first results about consistency of cross validated estimators of density functions have been obtained. The method of sieves and the principle of maximum likelihood have been used to develop algorithms for digital image processing. Specific applications include image segmentation, reconstruction methods for tomography, image registration methods for moving objects, and surface restoration algorithms. (Author)

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