Multiscale principal components analysis for image local orientation estimation

Author

Feng, XiaoGuang ; Milanfar, Peyman

Author_Institution

Dept. of Comput. Eng., California Univ., Santa Cruz, CA, USA

Volume

1

fYear

2002

fDate

3-6 Nov. 2002

Firstpage

478

Abstract

This paper presents an image local orientation estimation method, which is based on a combination of two already well-known techniques: the principal component analysis (PCA) and the multiscale pyramid decomposition. The PCA analysis is applied to find the maximum likelihood (ML) estimate of the local orientation. The proposed technique is shown to enjoy excellent robustness against noise. We present both simulated and real image examples to demonstrate the proposed technique.

Keywords

image processing; maximum likelihood estimation; principal component analysis; PCA analysis; image local orientation estimation; maximum likelihood estimation; multiscale pyramid decomposition; noise tolerance; principal component analysis; real images; robustness; Analytical models; Computational modeling; Computer simulation; Covariance matrix; Electronics packaging; Image analysis; Maximum likelihood estimation; Noise robustness; Principal component analysis; Singular value decomposition;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 2002. Conference Record of the Thirty-Sixth Asilomar Conference on

Conference_Location

Pacific Grove, CA, USA

ISSN

1058-6393

Print_ISBN

0-7803-7576-9

Type

conf

DOI

10.1109/ACSSC.2002.1197228

Filename

1197228