Elastic shapes models for improving segmentation of object boundaries in synthetic aperture sonar images
SAS imagery exhibits high levels of background noise and ambiguous target boundaries. Segmentation algorithms without prior shape knowledge typically fail. We model shapes as elements of a Riemannian manifold with an elastic metric. Such elastic shape models serve as shape priors in a Bayesian active contour model. We show improvement in boundary segmentation with shape prior versus without prior.
synthetic aperture sonar bayesian active contours riemannian geometry shape analysis