Title page for ETD etd-10222009-124949

Type of Document Master's Thesis
Author Mainguy, Yves
URN etd-10222009-124949
Title A robust variable order facet model for image data
Degree Master of Science
Department Computer Science and Applications
Advisory Committee
Advisor Name Title
Birch, Jeffrey B. Committee Co-Chair
Watson, Layne T. Committee Co-Chair
Ehrich, Roger W. Committee Member
  • Computer vision
Date of Defense 1991-12-05
Availability restricted

The underlying piecewise continuous surface of a digital image can be estimated through robust statistical procedures. This thesis contains a systematic Monte Carlo study of M-estimation and LMS estimation for image surface approximation, an examination of the merits of postprocessing and tuning various parameters in the robust estimation procedures, and a new robust variable order facet model paradigm. Several new goodness of fit measures are introduced, and systematically compared. It is shown that the M-estimation tuning parameters are not crucial, postprocessing is cheap and well worth the cost, and the robust variable order facet model algorithm (using M-estimation, new statistical goodness of fit measures, and postprocessing) manages to retain most of the statistical efficiency of Mestimation yet displays good robustness properties, and preserves the main geometric features of an image surface: step edges, roof edges and corners.

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