Minimum Many-to-Many Matchings for Computing the Distance Between Two Sequences

Mohamad, Mustafa A.; Rappaport, David; Toussaint, Godfried T.

doi:10.1007/s00373-014-1467-4

Cited by 3 publications

(1 citation statement)

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“…It is also planned to compare the measure tested here with other measures of geometric graph similarity. In particular, computing the many-to-many optimal matching for certain one-dimensional strings is computationally more efficient that the Hungarian algorithm for twodimensional geometric graphs (Eiter and Mannila, 1997), (Colannino et al, 2007), (Mohamad et al, 2014). Hence it is worth determining the viability of converting CPs to one-dimensional strings that can be tackled with one-dimensional many-to-many techniques.…”

Section: Discussionmentioning

confidence: 99%

A Dissimilarity Measure for Comparing Origami Crease Patterns

Toussaint

Demaine

et al. 2015

Proceedings of the International Conference on Pattern Recognition Applications and Methods

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A measure of dissimilarity (distance) is proposed for comparing origami crease patterns represented as geometric graphs. The distance measure is determined by minimum-weight matchings calculated between the edges as well as the vertices of the graphs being compared. The distances between pairs of edges and pairs of vertices of the graph are weighted linear combinations of six parameters that constitute geometric features of the edges and vertices. The results of a preliminary study performed with a collection of 45 crease patterns obtained from Mitani's ORIPA web page, revealed which of these features appear to be more salient for obtaining a clustering of the crease patterns that appears to agree with human intuition.

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Section: Discussionmentioning

confidence: 99%