Congruence, similarity and symmetries of geometric objects
Discrete & Computational Geometry - ACM Symposium on Computational Geometry, Waterloo
Efficient Pose Clustering Using a Randomized Algorithm
International Journal of Computer Vision
Combinatorial and experimental results for randomized point matching algorithms
Selected papers from the 12th annual symposium on Computational Geometry
Geometric matching under noise: combinatorial bounds and algorithms
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Computing Largest Common Point Sets under Approximate Congruence
ESA '00 Proceedings of the 8th Annual European Symposium on Algorithms
An O(v|v| c |E|) algoithm for finding maximum matching in general graphs
SFCS '80 Proceedings of the 21st Annual Symposium on Foundations of Computer Science
Algorithms for optimal protein structure alignment
Bioinformatics
On protein structure alignment under distance constraint
Theoretical Computer Science
Optimizing a Widely Used Protein Structure Alignment Measure in Expected Polynomial Time
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
On Complexity of Protein Structure Alignment Problem under Distance Constraint
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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The Largest Common Point-set (LCP) and the Pattern Matching (PM) problems have received much attention in the fields of pattern matching, computer vision and computational biology. Perhaps, the most important application of these problems is the protein structural alignment, which seeks to find a superposition of a pair of input proteins that maximizes a given protein structure similarity metric. Although it has been shown that LCP and PM are both tractable problems, the running times of existing algorithms are high-degree polynomials. Here, we present novel methods for finding approximate and exact threshold-LCP and threshold-PM for r-separated sets, in general, and protein 3D structures, in particular. Improved running times of our methods are achieved by building upon several different, previously published techniques.