TY - GEN

T1 - Asymptotic global confidence regions for 3-D parametric shape estimation in inverse problems

AU - Ye, Jong Chul

AU - Moulin, Pierre

AU - Bresler, Yoram

PY - 2005

Y1 - 2005

N2 - This paper derives fundamental performance bounds for estimating 3-D parametric surfaces in inverse problems. Un-like conventional pixel-based image reconstruction approaches, our problem is reconstruction of the shape of binary or homogeneous objects. The fundamental uncertainty of such estimation problems can be represented by global confidence regions, which facilitate geometric inference and optimization of the imaging system. Compared to two-dimensional global confidence region analysis in our previous work, computation of the probability that the entire 3-D surface estimate lies within the confidence region is, however, more challenging, because a surface estimate is an inhomogeneous random field continuously indexed by a two-dimensional index set. We derive an approximate lower bound to this probability using the so-called tube formula for the tail probability of a Gaussian random field. Simulation results demonstrate the tightness of the resulting bound and the usefulness of 3-D global confidence region approach.

AB - This paper derives fundamental performance bounds for estimating 3-D parametric surfaces in inverse problems. Un-like conventional pixel-based image reconstruction approaches, our problem is reconstruction of the shape of binary or homogeneous objects. The fundamental uncertainty of such estimation problems can be represented by global confidence regions, which facilitate geometric inference and optimization of the imaging system. Compared to two-dimensional global confidence region analysis in our previous work, computation of the probability that the entire 3-D surface estimate lies within the confidence region is, however, more challenging, because a surface estimate is an inhomogeneous random field continuously indexed by a two-dimensional index set. We derive an approximate lower bound to this probability using the so-called tube formula for the tail probability of a Gaussian random field. Simulation results demonstrate the tightness of the resulting bound and the usefulness of 3-D global confidence region approach.

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M3 - Conference contribution

AN - SCOPUS:33947186816

SN - 0780394046

SN - 9780780394049

T3 - IEEE Workshop on Statistical Signal Processing Proceedings

SP - 471

EP - 476

BT - 2005 IEEE/SP 13th Workshop on Statistical Signal Processing - Book of Abstracts

T2 - 2005 IEEE/SP 13th Workshop on Statistical Signal Processing

Y2 - 17 July 2005 through 20 July 2005

ER -