Nonparametric Estimation by the Method of Sieves.

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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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1983
Accession Number
ADA131394

Entities

People

  • Donald E. Mcclure
  • Stuart Geman

Organizations

  • Brown University

Tags

Communities of Interest

  • Autonomy
  • Biomedical
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Computational Science
  • Computer Graphics
  • Computers
  • Data Science
  • Digital Images
  • Estimators
  • Factor Analysis
  • Image Processing
  • Image Segmentation
  • Information Processing
  • Information Science
  • Maximum Likelihood Estimation
  • Probability
  • Statistical Algorithms
  • Two Dimensional

Readers

  • Computer Vision.
  • Statistical inference.

Technology Areas

  • Space
  • Space - Space Objects