Sciweavers

ICIP
2002
IEEE

Complexity regularized shape estimation from noisy Fourier data

14 years 5 months ago
Complexity regularized shape estimation from noisy Fourier data
We consider the estimation of an unknown arbitrary 2D object shape from sparse noisy samples of its Fourier transform. The estimate of the closed boundarycurve 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.
Natalia A. Schmid, Yoram Bresler, Pierre Moulin
Added 24 Oct 2009
Updated 27 Oct 2009
Type Conference
Year 2002
Where ICIP
Authors Natalia A. Schmid, Yoram Bresler, Pierre Moulin
Comments (0)