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Published Articles >> Table of Contents >> Abstract
2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01) - Volume 2
p. 448
Necessary Conditions to Attain Performance Bounds on Structure and Motion Estimates of Rigid Objects
Margrit Betke, Boston University
Eran Naftali, Massachusetts Institute of Technology
Nicholas C. Makris, Massachusetts Institute of Technology
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CVPR.2001.990996
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| Abstract |
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A nalyticconditions that are necessary for the maximum likelihood estimate to become asymptotically unbiased and attain minimum variance are derived for
estimation problems in computer vision. In particular,
problems of estimating the parameters that describe the
3D structure of rigid objects or their motion are inves-
tigated. It is common practice to compute Cramer-Rao
lower bounds (CRLB) to approximate the mean-square
error in parameter estimation problems, but the CRLB
is not guaranteed to be a tight bound and typically underestimates the true mean-square error. The necessary conditions for the Cramer-Rao lower bound to be
a good approximation of the mean-square error are derived. The tightness of the bound depends on the noise
level, the number of pixels on the surface of the object,
and the texture of the surface. We examine our analytical results experimentally using polyhedral objects that
consist of planar surfac epatches with various textures
that move in 3D space. We provide necessary conditions for the CRLB to be attained that depend on the
size, texture, and noise level of the surface patch.
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 Additional Information
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Citation:
Margrit Betke, Eran Naftali, Nicholas C. Makris,
"Necessary Conditions to Attain Performance Bounds on Structure and Motion Estimates of Rigid Objects,"
cvpr,
p. 448,
2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01) - Volume 2,
2001
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