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carole Twining and Chris Taylor (2001)

Kernel principal Component Analysis and the construction of non-linear Active Shape Models

In: British Machine Vision Conference, vol. 1, pp. 23-32.

The use of Kernel PCA to model data distributions in high-dimensional spaces is described. Of the many potential applications, we focus on the problem of modelling the variability in a class of shapes. We show that a previous approach to representing non-linear shape comstraints using KPCA is not generally valid, and intriduce a new proximity-to-data measure that behaves correctly. This measure is applied to the building of models of both synthetic and real shapes of nematode worms. It is shown that using such a model to impose shape constraints during Active Shape Model search gives improved segmentations of worm images than those obtained using linear shape constraints.

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