An Experimental Comparison of Discrete and Continuous Shape Optimization Methods

Shape optimization is a problem which arises in numerous computer vision problems such as image segmentation and multiview reconstruction. In this paper, we focus on a certain class of binary labeling problems which can be globally optimized both in a spatially discrete setting and in a spatially continuous setting. The main contribution of this paper is to present a quantitative comparison of the reconstruction accuracy 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 L2-norm in the length term.

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Maria Klodt, Thomas Schoenemann, Kalin Kolev, Marek Schikora, Daniel Cremers
• Keywords: Binary Labeling Problems, Computer Vision, Cut Based Framework, ECCV 2008, Length Regularity, Multiview Reconstruction, Pairwise Terms