Complexity regularized shape estimation from noisy fourier data

Natalia A. Schmid, Yoram Bresler, Pierre Moulin

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


We consider the estimation of an unknown arbitrary 2D object shape from sparse noisy samples of its Fourier transform. The estimate of the closed boundary curve is parametrized by normalized Fourier descriptors (FDs). We use Rissanen's MDL criterion to regularize this ill-posed non-linear inverse problem and determine an optimum tradeoff between approximation and estimation errors by picking an optimum order for the FD parametrization. The performance of the proposed estimator is quantified in terms of the area discrepancy between the true and estimated object. Numerical results demonstrate the effectiveness of the proposed approach.

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

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


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