Abstract

Shape optimization is a problem which arises in numerous computer vision problems such as image segmentation and multiview re- construction. In this paper, we focus on a certain class of binary labeling problems which can be globally optimized both in a spatially discrete set- ting and in a spatially continuous setting. The main contribution of this paper is to present a quantitative comparison of the reconstruction accu- racy and computation times which allows to assess some of the strengths and limitations of both approaches. We also present a novel method to approximate length regularity in a graph cut based framework: Instead of using pairwise terms we introduce higher order terms. These allow to represent a more accurate discretization of the -norm in the length term.