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Cluster Based non-linear Principle Component Analysis

Bowden, R, Mitchell, TA and Sarhadi, M (1997) Cluster Based non-linear Principle Component Analysis Electronics Letters, 33 (22). pp. 1858-1859.

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Abstract

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.

Item Type: Article
Authors :
AuthorsEmailORCID
Bowden, RUNSPECIFIEDUNSPECIFIED
Mitchell, TAUNSPECIFIEDUNSPECIFIED
Sarhadi, MUNSPECIFIEDUNSPECIFIED
Date : 23 October 1997
Identification Number : https://doi.org/10.1049/el:19971300
Copyright Disclaimer : © 1997 The Institution of Engineering and Technology
Uncontrolled Keywords : Computer vision, Cluster tools, Image processing
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 15:33
Last Modified : 28 Mar 2017 15:33
URI: http://epubs.surrey.ac.uk/id/eprint/812656

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