Statistics of surface curvature estimates

Author

Hilton, A. ; Illingworth, J. ; Windeatt, T.

Author_Institution

Dept. of Electron. & Electr. Eng., Surrey Univ., Guildford, UK

Volume

1

fYear

1994

fDate

9-13 Oct 1994

Firstpage

37

Abstract

Reliable curvature estimation is an important goal in image analysis to provide viewpoint independent cues for shape classification. This paper presents a model of the relationship between the variance of curvature estimates and the image noise. Agreement to within 10% is obtained for 3D range data. Previous models have only provided qualitative agreement with experimental observations. A perturbation error analysis is performed on the local least square surface fitting algorithm which is commonly used to obtain partial derivative estimates in the presence of noise

Keywords

partial differential equations; 3D range data; image analysis; image noise; least square surface fitting algorithm; partial derivatives; perturbation error analysis; shape classification; surface curvature estimates; Error analysis; Image analysis; Image segmentation; Least squares approximation; Least squares methods; Noise shaping; Polynomials; Shape; Statistics; Surface fitting;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 1994. Vol. 1 - Conference A: Computer Vision & Image Processing., Proceedings of the 12th IAPR International Conference on

Conference_Location

Jerusalem

Print_ISBN

0-8186-6265-4

Type

conf

DOI

10.1109/ICPR.1994.576222

Filename

576222