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Point-Placement Error in Shape Models

Here we analyse the effects of the error involved in manually placing “mark-up” points in active shape models. We demonstrate that the magnitudes of eigenvalues retained in principal components analysis (PCA) are increased by point-placement error. We present a revised covariance matrix that reduces or completely removes the effects of point-placement error. We find results for “predicted” placement errors that are in excellent agreement with the “set-up” parameters of simulations for an elliptical shape. We find that increasing point-placement error in the original “training set” increases point-toline and point-to-point errors in ASM image searches and adversely affects convergence.