On the reconstruction aspects of moment descriptors

Author

Pawlak, Miroslaw

Author_Institution

Dept. of Electr. & Comput. Eng., Manitoba Univ., Winnipeg, Man., Canada

Volume

38

Issue

6

fYear

1992

fDate

11/1/1992 12:00:00 AM

Firstpage

1698

Lastpage

1708

Abstract

The problem of reconstruction of an image from discrete and noisy data by the method of moments is examined. The set of orthogonal moments based on Legendre polynomials is employed. A general class of signal-dependent noise models is taken into account. An asymptotic expansion for the global reconstruction error is established. This reveals mutual relationships between a number of moments, the image smoothness, sampling rate, and noise model characteristics. The problem of an automatic (data-driven) section of an optimal number of moments is studied. This is accomplished with the help of cross-validation techniques

Keywords

image reconstruction; polynomials; Legendre polynomials; asymptotic expansion; cross-validation techniques; discrete data; global reconstruction error; image reconstruction; image smoothness; method of moments; moment descriptors; noisy data; orthogonal moments; sampling rate; signal-dependent noise models; Computer errors; Convergence; Image reconstruction; Image sampling; Layout; Marine vehicles; Moment methods; Pattern matching; Pattern recognition; Polynomials;

fLanguage

English

Journal_Title

Information Theory, IEEE Transactions on

Publisher

ieee

ISSN

0018-9448

Type

jour

DOI

10.1109/18.165444

Filename

165444