Covariance methods in computer vision

Author

Liu, Zhi-Qiang ; Madiraju, Sharma ; Kitchen, Les ; Dance, Sandy

Author_Institution

Dept. of Comput. Sci., Melbourne Univ., Parkville, Vic., Australia

Volume

2

fYear

1996

fDate

14-18 Oct 1996

Firstpage

851

Abstract

We discuss the use of covariance methods in invariant feature extraction, texture segmentation, edge detection, and surface geometry analysis. The covariance technique is used to compute local descriptors and to index roughness, anisotropy, or general textural differences. We also present a simple yet effective edge detection algorithm using a neural network which is trained by invariant features generated from covariance matrices

Keywords

computer vision; covariance matrices; edge detection; feature extraction; image representation; image segmentation; image texture; learning (artificial intelligence); neural nets; anisotropy; computer vision; covariance matrices; covariance methods; edge detection; edge detection algorithm; feature representation method; invariant feature extraction; local descriptors; neural network; roughness; surface geometry analysis; textural differences; texture segmentation; Anisotropic magnetoresistance; Computer vision; Covariance matrix; Feature extraction; Geometry; Image edge detection; Neural networks; Rough surfaces; Surface roughness; Surface texture;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing, 1996., 3rd International Conference on

Conference_Location

Beijing

Print_ISBN

0-7803-2912-0

Type

conf

DOI

10.1109/ICSIGP.1996.566219

Filename

566219