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Cluster based nonlinear principle component analysis

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3 Author(s)
Bowden, R. ; Dept. of Manuf. & Eng. Syst., Brunel Univ., Uxbridge, UK ; Mitchell, T.A. ; Sarhadi, M.

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

Published in:

Electronics Letters  (Volume:33 ,  Issue: 22 )

Date of Publication:

23 Oct 1997

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