Results 211 to 220 of about 91,504 (262)
Computing double-pushout graph transformation rules and atom-to-atom maps from KEGG RCLASS data. [PDF]
Algorithms Mol BiolBeier N +3 more
europepmc +1 more source
Hybrid Deep Learning Model for EI-MS Spectra Prediction. [PDF]
Int J Mol SciMajewski B, Łabuda M.
europepmc +1 more source
A Graph-Based Algorithm for Computing Matrix Elements of Arbitrary Operators between Configuration State Functions. [PDF]
J Phys Chem AFdez Galván I, Rooein M, Lindh R.
europepmc +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
PAMM, 2007
AbstractGraphs are important structures to model complex relationships such as chemical compounds, proteins, geometric or hierarchical parts, and XML documents. Given a query graph, indexing has become a necessity to retrieve similar graphs quickly from large databases.
Demirci, M.F. +2 more
openaire +2 more sources
AbstractGraphs are important structures to model complex relationships such as chemical compounds, proteins, geometric or hierarchical parts, and XML documents. Given a query graph, indexing has become a necessity to retrieve similar graphs quickly from large databases.
Demirci, M.F. +2 more
openaire +2 more sources
IEEE Transactions on Cybernetics, 2018
Establishing correspondence between point sets lays the foundation for many computer vision and pattern recognition tasks. It can be well defined and solved by graph matching. However, outliers may significantly deteriorate its performance, especially when outliers exist in both point sets and meanwhile the inlier number is unknown.
Xu Yang, Zhi-Yong Liu
openaire +2 more sources
Establishing correspondence between point sets lays the foundation for many computer vision and pattern recognition tasks. It can be well defined and solved by graph matching. However, outliers may significantly deteriorate its performance, especially when outliers exist in both point sets and meanwhile the inlier number is unknown.
Xu Yang, Zhi-Yong Liu
openaire +2 more sources
Journal of Graph Theory, 1998
Summary: The matching graph \(M(G)\) of a graph \(G\) is that graph whose vertices are the maximum matchings in \(G\) and where two vertices \(M_1\) and \(M_2\) of \(M(G)\) are adjacent if and only if \(| M_1- M_2|= 1\). When \(M(G)\) is connected, this graph models a metric space whose metric is defined on the set of maximum matchings in \(G\).
Eroh, Linda, Schultz, Michelle
openaire +2 more sources
Summary: The matching graph \(M(G)\) of a graph \(G\) is that graph whose vertices are the maximum matchings in \(G\) and where two vertices \(M_1\) and \(M_2\) of \(M(G)\) are adjacent if and only if \(| M_1- M_2|= 1\). When \(M(G)\) is connected, this graph models a metric space whose metric is defined on the set of maximum matchings in \(G\).
Eroh, Linda, Schultz, Michelle
openaire +2 more sources
Kronecker product graph matching
Pattern Recognition, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
van Wyk, B. J., van Wyk, M. A.
openaire +1 more source
IEEE Transactions on Medical Imaging, 2009
Separating bone, calcification, and vessels in computer tomography angiography (CTA) allows for a detailed diagnosis of vessel stenosis. This paper presents a new, graph-based technique that solves this difficult problem with high accuracy. The approach requires one native data set and one that is contrast enhanced.
Dmitry, Maksimov +8 more
openaire +2 more sources
Separating bone, calcification, and vessels in computer tomography angiography (CTA) allows for a detailed diagnosis of vessel stenosis. This paper presents a new, graph-based technique that solves this difficult problem with high accuracy. The approach requires one native data set and one that is contrast enhanced.
Dmitry, Maksimov +8 more
openaire +2 more sources
INEXACT GRAPH MATCHING THROUGH GRAPH COVERAGE
Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods, 2012In this paper we propose a novel inexact graph matching procedure called graph coverage, to be used in supervised and unsupervised data driven modeling systems. It relies on tensor product between graphs, since the resulting product graph is known to be able to encode the similarity of the two input graphs. The graph coverage is defined on the basis of
LIVI, LORENZO +2 more
openaire +1 more source
2020
Abstract This chapter explores parallel algorithms for graph matching. Here, a graph is the mathematical representation of a network, with vertices representing the nodes of the network and edges representing their connections. The edges have positive weights, and the aim is to find a matching with maximum total weight. The chapter first
openaire +1 more source
Abstract This chapter explores parallel algorithms for graph matching. Here, a graph is the mathematical representation of a network, with vertices representing the nodes of the network and edges representing their connections. The edges have positive weights, and the aim is to find a matching with maximum total weight. The chapter first
openaire +1 more source