Cramér-Rao bounds for parametric shape estimation in inverse problems

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We address the problem of computing fundamental performance bounds for estimation of object boundaries from noisy measurements in inverse problems, when the boundaries are parameterized by a finite number of unknown variables. Our model applies to multiple unknown objects, each with its own unknown gray level, or color, and boundary parameterization, on an arbitrary known background. While such fundamental bounds on the performance of shape estimation algorithms can in principle be derived from the Cramér-Rao lower bounds, very few results have been reported due to the difficulty of computing the derivatives of a functional with respect to shape deformation. In this paper, we provide a general formula for computing Cramér-Rao lower bounds in inverse problems where the observations are related to the object by a general linear transform, followed by a possibly nonlinear and noisy measurement system.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Image Processing
StatePublished - 2002
EventInternational Conference on Image Processing (ICIP'02) - Rochester, NY, United States
Duration: Sep 22 2002Sep 25 2002


OtherInternational Conference on Image Processing (ICIP'02)
Country/TerritoryUnited States
CityRochester, NY

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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