Shape classification using smooth principal components

We suggest and assess a novel approach to shape classification using smooth functional principal components. This gives a rotation, location and scale invariant classifier. Our experiments show that this approach can outperform a number of competitors including conventional Eigenshape methods and time series methods. The degree of smoothing associated with the best classification performance is determined automatically using cross-validation.

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