Point distribution model: Difference between revisions

Revision as of 06:18, 20 October 2020

Revision as of 15:48, 11 January 2022

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Due to the PCA properties: eigenvectors are mutually [[orthogonal]], form a basis of the training set cloud in the shape space, and cross at the 0 in this space, which represents the mean shape. Also, PCA is a traditional way of fitting a closed ellipsoid to a Gaussian cloud of points (whatever their dimension): this suggests the concept of bounded variation.

The idea behind PDM'sPDMs is that eigenvectors can be linearly combined to create an infinity of new shape instances that will 'look like' the one in the training set. The coefficients are bounded alike the values of the corresponding eigenvalues, so as to ensure the generated 2n/3n-dimensional dot will remain into the hyper-ellipsoidal allowed ___domain—[[allowable shape ___domain]] (ASD).<ref name=taylor/>

==See also==