Cluster based nonlinear principle component analysis

被引：9

作者：

Bowden, R ^{[1
]}

Mitchell, TA ^{[1
]}

Sarhadi, M ^{[1
]}

机构：

[1] Brunel Univ, Dept Mfg & Engn Syst, Uxbridge UB8 3PH, Middx, England

来源：

ELECTRONICS LETTERS | 1997年 / 33卷 / 22期

关键词：

computer vision; cluster tools; image processing;

D O I：

10.1049/el:19971300

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In the field of computer vision, principle component analysis (PCA) is often used to provide statistical models of shape, deformation or appearance. This simple statistical model provides a constrained. compact approach to model based vision. However, as larger problems are considered. high dimensionality and nonlinearity make linear PCA an unsuitable and unreliable approach. A nonlinear PCA (NLPCA) technique is proposed which uses cluster analysis and dimensional reduction to provide a fast. robust solution. Simulation results on both 2D contour models and greyscale images are presented.

引用

页码：1858 / 1859

页数：2