Invariant extraction and segmentation of 3D objects using linear Lie algebra model

Author

Chao, Junhui ; Suzuki, Masaki

Author_Institution

Dept. of Electr., Electron. Eng., & Commun., Chuo Univ., Tokyo, Japan

Volume

1

fYear

2002

fDate

2002

Abstract

This paper first presents robust algorithms to extract invariants of the linear Lie algebra model from 3D objects. In particular, an extended 3D Hough transform is presented to extract accurate estimates of the normal vectors. Least squares fitting is used to find normal vectors and representation matrices. Then a segmentation algorithm for 3D objects is shown using the invariants of linear Lie algebra. Distributions of invariants, both in the invariant space and on the object surface, are used to produce clusters and edge detection.

Keywords

Hough transforms; Lie algebras; edge detection; feature extraction; image representation; image segmentation; least squares approximations; matrix algebra; parameter estimation; pattern clustering; solid modelling; 3D computer graphics; 3D object segmentation; Hough transform; edge detection; image recognition; invariant extraction; least squares fitting; linear Lie algebra model; normal vectors; representation matrices; Algebra; Chaotic communication; Clustering algorithms; Image coding; Image recognition; Image segmentation; Least squares methods; Robustness; Shape; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing. 2002. Proceedings. 2002 International Conference on

ISSN

1522-4880

Print_ISBN

0-7803-7622-6

Type

conf

DOI

10.1109/ICIP.2002.1037995

Filename

1037995