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
Keywords	Computer vision
Date of Defense	1991-12-05
Availability	restricted
Abstract 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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