Shape prior based object extraction using a log-polar domian representation

In this paper, we address the problem of extracting objects in an image that conform to prior object shape knowledge. Major challenges include the judgment of scale and rotation parameters as well as tolerating occlusions and noise. We propose the use of a novel log polar domain mapping of the Cartesian domain image to efficiently and effectively overcome these. This method greatly simplifies rotation, scaling and provides an opportunity to incorporate a decision threshold. Whilst, the initial theoretical framework is developed on binary images, placement of this into the gray scale domain is achieved by incorporating a pre-processing binary segmentation step. A post processing Cartesian domain energy optimization is done to counteract discripancies caused at the initial stage. We demonstrate our results using several examples.