A Ground Truth Correspondence Measure for Benchmarking

Automatic localisation of correspondences for the construction of statistical shape models from examples has been the focus of intense research during the last decade. Several algorithms are available and benchmarking is needed to rank the different algorithms. Prior work has focused on evaluating the quality of the models produced by the algorithms by measuring compactness, generality and specificity. In this paper problems with these standard measures are discussed. We propose that a ground truth correspondence measure (gem) is used for benchmarking and in this paper benchmarking is performed on several state of the art algorithms. Minimum description length (MDL) with a curvature cost comes out as the winner of the automatic methods. Hand marked models turn out to be best but a semi-automatic method is shown to lie in between the best automatic method and the hand built models in performance