Invariant shape prior knowledge for an edge-based active contours invariant shape prior for active contours

In this paper, we intend to propose a new method to incorporate geometric shape prior into an edge-based active contours for robust object detection in presence of partial occlusions, low contrast and noise. A shape registration method based on phase correlation of binary images, associated with level set functions of the active contour and a reference shape, is used to define prior knowledge making the model invariant with respect to Euclidean transformations. In case of several templates, a set of complete invariant shape descriptors is used to select the most suitable one according to the evolving contour. Experimental results show the ability of the proposed approach to constrain an evolving curve towards a target shapes that may be occluded and cluttered under rigid transformations.