Formulation and Evaluation of Variational Curve Matching with Prior Constraints
This work explores a priori constraints based on human landmarking for defining the parameters of a variational curve registration algorithm. The result is a method for designing variational energies that adjust the optimization process over the solution domain such that features which are salient in the human decision-making process are used. The application here is locating correspondence of corpora callosa that agree with expert user data. General principles that guide our particular application are first stated. These principles involve the definition of a generic variational problem and an associated optimization algorithm, given here for the case of matching curves. A small set of specific similarity criterion for curves is then defined. The ability of each feature to find correspondences that relate to human decisions is individually tested. Following the results of this study, a Maximum a Posteriori (MAP) automatic landmarking and correspondence method is developed. The probabilities associated with the MAP estimates are also used to define variational weights that vary over the solution’s domain. These methods are both evaluated with respect to known correspondences and inter-user variability.
KeywordsVariational Energy Prior Weight Active Shape Model Descriptor Function Curve Match
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