Incorporating shape prior into geodesic active contours for detecting partially occluded object

A new method to incorporate shape prior knowledge into geodesic active contours for detecting partially occluded object is proposed in this paper. The level set functions of the collected shapes are used as training data. They are projected onto a low dimensional subspace using PCA and their distribution is approximated by a Gaussian function. A shape prior model is constructed and is incorporated into the geodesic active contour formulation to constrain the contour evolution process. To balance the strength between the image gradient force and the shape prior force, a weighting factor is introduced to adaptively guide the evolving curve to move under both forces. The curve converges with due consideration of both local shape variations and global shape consistency. Experimental results demonstrate that the proposed method makes object detection robust against partial occlusions.