An Experimental Comparison of Discrete and Continuous Shape Optimization Methods [chapter]

Maria Klodt, Thomas Schoenemann, Kalin Kolev, Marek Schikora, Daniel Cremers
<span title="">2008</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
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
more &raquo; ... tations 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-540-88682-2_26">doi:10.1007/978-3-540-88682-2_26</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jdxsk6ecvrd5lakoryknzb4yna">fatcat:jdxsk6ecvrd5lakoryknzb4yna</a> </span>
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