Image Segmentation with Elastic Shape Priors via Global Geodesics in Product Spaces
Thomas Schoenemann, F.R. Schmidt, Daniel Cremers · 2008
We propose an efficient polynomial time algorithm to match an elastically deforming shape to an image. It is based on finding a globally optimal geodesic in the product space spanned by the image and the prior contour. To this end a branch-and-bound scheme is combined with shortest path techniques. We compare this algorithm with a recently proposed ratio minimization approach. While we show that generally the ratio is the better model, for many instances the two perform similarly. We identify a class of problems where the proposed method is likely to be faster. 1 Introduction and Related Work For decades researchers have striven to develop machine vision algorithms which can compete with or even outperform the human visual system. Despite many efforts this remains a challenging problem. The human visual system makes heavily use of prior world knowledge. As a consequence