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dc.contributor.authorKhan, N.en
dc.contributor.authorNeri, Ferranteen
dc.contributor.authorAhmadi, Samaden
dc.date.accessioned2016-04-18T10:18:16Z
dc.date.available2016-04-18T10:18:16Z
dc.date.issued2015-12-07
dc.identifier.citationKhan, N., Neri, F. and Ahmadi, S. (2015) Adaptive Differential Evolution Applied to Point Matching 2D GIS Data. 2015 IEEE Symposium Series on Computational Intelligence, pp. 1719-1726en
dc.identifier.isbn9781479975600
dc.identifier.urihttp://hdl.handle.net/2086/11905
dc.description.abstractThe impetus behind data analytics and integration is the need for greater insight and data visibility, but since a growing share of our data is multimedia, there is a parallel need for methods that can align multimedia data. This paper explores georeferencing, which is used to combine spatial datasets and used here to align map images to 2D GIS models. This paper surveys various approaches for building the key components of a georeferencing solution, notes their strengths and weaknesses, and comments on their trajectory to help orient future work. The implementation presented here uses Hough transforms for feature detection, nearest neighbor correspondences with simplistic similarity measures, and a population based optimizer. The comparison among metaheuristics has shown that Differential Evolution (DE) frameworks appear especially suited for this problem. In particular, the controlled randomization of DE parameters appears to display the best performance in terms of execution time and competitive performance in terms of function evaluations even with respect to more complex memetic implementations.en
dc.language.isoenen
dc.publisherIEEEen
dc.titleAdaptive Differential Evolution Applied to Point Matching 2D GIS Dataen
dc.typeConferenceen
dc.identifier.doihttp://dx.doi.org/10.1109/SSCI.2015.241
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceN/Aen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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