Boundary Finding with Parametrically Deformable Models

Segmentation using boundary finding is enhanced both by considering the boundary as a whole and by using model-based global shape information. Previous boundary finding methods have either not used global shape or have designed individual shape models specific to particular shapes. We apply flexible constraints, in the form of a probabilistic deformable model, to the problem of segmenting natural 2-D objects whose diversity and irregularity of shape make them poorly represented in terms of fixed features or form. The parametric model is based on the elliptic Fourier decomposition of the boundary. Probability distributions on the parameters of the representation bias the model to a particular overall shape while allowing for deformations. Boundary finding is formulated as an optimization problem using a maximum a posteriori objective function. Results of the method applied to real and synthetic images are presented, including an evaluation of the dependence of the method on prior information and image quality.