Incremental matrix orthogonalization with an application to curve fitting

被引：6

作者：

Harker, M ^{[1
]}

O'Leary, P ^{[1
]}

Zsombor-Murray, P ^{[1
]}

机构：

[1] Inst Automat, A-8700 Leoben, Austria

来源：

Computational Imaging III | 2005年 / 5674卷

关键词：

matrix approximation; fitting; implicit curves; shape identification and classification; LEAST-SQUARES;

D O I：

10.1117/12.586499

中图分类号：

TB8 [摄影技术];

学科分类号：

0804 ;

摘要：

A new method for fitting implicit curves to scattered data is proposed. The method is based on orthogonal matrix projections and singular value decomposition. The incremental aspect of the algorithm deals with each order of data individually in an incrementing manner, whereby a matrix approximation procedure is applied at each level. This determines the fit quality at each step, and hence provides co-linearity detection of each polynomial order. The best implicit polynomial fit of minimal order is provided, which essentially combines object identification and classification with object fitting.

引用

页码：354 / 361

页数：8

共 6 条

[1] Revisiting Hartley's normalized eight-point algorithm [J].