caa a22 4500 605540810 CHVBK 20210128100913.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s00371-015-1115-x doi (NATIONALLICENCE)springer-10.1007/s00371-015-1115-x Graph-based deformable matching of 3D line with application in protein fitting [Elektronische Daten] [Hang Dou, Matthew Baker, Tao Ju] We present an algorithm for matching two sets of line segments in 3D that have undergone non-rigid deformations. This problem is motivated by a biology application that seeks a correspondence between the alpha-helices from two proteins, so that matching helices have similar lengths and these can be aligned by some low-distortion deformation. While matching between two feature sets have been extensively studied, particularly for point features, matching line segments has received little attention so far. As typical in point-matching methods, we formulate a graph matching problem and solve it using continuous relaxation. We make two technical contributions. First, we propose a graph construction for undirected line segments such that the optimal matching between two graphs represents an as-rigid-as-possible deformation between the two sets of segments. Second, we propose a novel heuristic for discretizing the continuous solution in graph matching. Our heuristic can be applied to matching problems (such as ours) that are not amenable to certain heuristics, and it produces better solutions than those applicable heuristics. Our method is compared with a state-of-art method motivated by the same biological application and demonstrates improved accuracy. Springer-Verlag Berlin Heidelberg, 2015 Non-rigid nationallicence Graph matching nationallicence Quadratic assignment nationallicence Line feature nationallicence Dou Hang Washington University in St. Louis, St. Louis, USA aut Baker Matthew Baylor College of Medicine, Houston, USA aut Ju Tao Washington University in St. Louis, St. Louis, USA aut The Visual Computer Springer Berlin Heidelberg 31/6-8(2015-06-01), 967-977 0178-2789 31:6-8<967 2015 31 371 https://doi.org/10.1007/s00371-015-1115-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00371-015-1115-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Dou Hang Washington University in St. Louis, St. Louis, USA aut NATIONALLICENCE 700 1- Baker Matthew Baylor College of Medicine, Houston, USA aut NATIONALLICENCE 700 1- Ju Tao Washington University in St. Louis, St. Louis, USA aut NATIONALLICENCE 773 0- The Visual Computer Springer Berlin Heidelberg 31/6-8(2015-06-01), 967-977 0178-2789 31:6-8<967 2015 31 371