Learning a New Statistical Shape Prior Model for Object Detection by Geodesic Active Contours

Abstract

A new statistical shape prior model is proposed in this paper which is incorporated into geodesic active contours for robust object detection. The object shapes that undergo nonlinear deformable changes are assumed to lie in a low dimensional feature subspace and form clusters after a nonlinear mapping. They are approximated by a probabilistic density model to explore the structure of data distribution. The obtained probability is treated as a shape energy term and is incorporated into geodesic active contour equation to constrain the further curve evolution process. This shape prior model is based on a more sophisticated statistical learning of the training data distribution and thus is more robust in presence of occlusions and cluttered background. Experiments demonstrate its promising detection performance for the intended tasks.