Estimating the Confidence of Statistical Model Based Shape Prediction

We propose a method for estimating confidence regions around shapes predicted from partial observations, given a statistical shape model. Our method relies on the estimation of the distribution of the prediction error, obtained non-parametrically through a bootstrap resampling of a training set. It can thus be easily adapted to different shape prediction algorithms. Individual confidence regions for each landmark are then derived, assuming a Gaussian distribution. Merging those individual confidence regions, we establish th probability that, on average, a given proportion of the predicted landmarks actually lie in their estimated regions. We also propose a method for validating the accuracy of these regions using a test set.