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dc.contributor.authorLopes, Raul
dc.contributor.authorFranqueira, Virginia N. L.
dc.contributor.authorReid, Ivan D.
dc.contributor.authorHobson, Peter
dc.date.accessioned2017-11-27T09:51:42Z
dc.date.available2017-11-27T09:51:42Z
dc.date.issued2017-11
dc.identifier.citationLopes, R. et al (2017) 'Parallel Monte Carlo Search for Hough Transform', Journal of Physics: Conference Series, 898:072052.en
dc.identifier.issn17426588
dc.identifier.doi10.1088/1742-6596/898/7/072052
dc.identifier.urihttp://hdl.handle.net/10545/621978
dc.description.abstractWe investigate the problem of line detection in digital image processing and in special how state of the art algorithms behave in the presence of noise and whether CPU efficiency can be improved by the combination of a Monte Carlo Tree Search, hierarchical space decomposition, and parallel computing. The starting point of the investigation is the method introduced in 1962 by Paul Hough for detecting lines in binary images. Extended in the 1970s to the detection of space forms, what came to be known as Hough Transform (HT) has been proposed, for example, in the context of track fitting in the LHC ATLAS and CMS projects. The Hough Transform transfers the problem of line detection, for example, into one of optimization of the peak in a vote counting process for cells which contain the possible points of candidate lines. The detection algorithm can be computationally expensive both in the demands made upon the processor and on memory. Additionally, it can have a reduced effectiveness in detection in the presence of noise. Our first contribution consists in an evaluation of the use of a variation of the Radon Transform as a form of improving theeffectiveness of line detection in the presence of noise. Then, parallel algorithms for variations of the Hough Transform and the Radon Transform for line detection are introduced. An algorithm for Parallel Monte Carlo Search applied to line detection is also introduced. Their algorithmic complexities are discussed. Finally, implementations on multi-GPU and multicore architectures are discussed.
dc.description.sponsorshipLopes, Reid and Hobson are members of the GridPP collaboration and wish to acknowledge funding from the Science and Technology Facilities Council, UK.en
dc.language.isoenen
dc.publisherIOP Publishing Ltden
dc.relation.urlhttp://stacks.iop.org/1742-6596/898/i=7/a=072052?key=crossref.72fb83a34336cbb64665d1ee42abb8b4en
dc.rightsArchived with thanks to Journal of Physics: Conference Seriesen
dc.subjectImage processingen
dc.subjectParallel computingen
dc.subjectComputer performanceen
dc.subjectComputingen
dc.titleParallel Monte Carlo search for Hough Transform.en
dc.typeArticleen
dc.identifier.eissn17426596
dc.contributor.departmentBrunel University Londonen
dc.contributor.departmentUniversity of Derbyen
dc.identifier.journalJournal of Physics: Conference Seriesen
refterms.dateFOA2019-02-28T16:17:49Z
html.description.abstractWe investigate the problem of line detection in digital image processing and in special how state of the art algorithms behave in the presence of noise and whether CPU efficiency can be improved by the combination of a Monte Carlo Tree Search, hierarchical space decomposition, and parallel computing. The starting point of the investigation is the method introduced in 1962 by Paul Hough for detecting lines in binary images. Extended in the 1970s to the detection of space forms, what came to be known as Hough Transform (HT) has been proposed, for example, in the context of track fitting in the LHC ATLAS and CMS projects. The Hough Transform transfers the problem of line detection, for example, into one of optimization of the peak in a vote counting process for cells which contain the possible points of candidate lines. The detection algorithm can be computationally expensive both in the demands made upon the processor and on memory. Additionally, it can have a reduced effectiveness in detection in the presence of noise. Our first contribution consists in an evaluation of the use of a variation of the Radon Transform as a form of improving theeffectiveness of line detection in the presence of noise. Then, parallel algorithms for variations of the Hough Transform and the Radon Transform for line detection are introduced. An algorithm for Parallel Monte Carlo Search applied to line detection is also introduced. Their algorithmic complexities are discussed. Finally, implementations on multi-GPU and multicore architectures are discussed.


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