Title page for ETD etd-10222009-124949
|Type of Document
||A robust variable order facet model for image data
||Master of Science
||Computer Science and Applications
|Birch, Jeffrey B.
|Watson, Layne T.
|Ehrich, Roger W.
|Date of Defense
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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