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dc.contributor.authorArranz, Álvaro
dc.contributor.authorAlvar, Manuel
dc.date2015-03
dc.date.accessioned2020-04-23T07:40:09Z
dc.date.available2020-04-23T07:40:09Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/9999
dc.description.abstractDuring the last decade, the general-purpose computing on graphics processing units Graphics (GPGPU) has turned out to be a useful tool for speeding up many scientific calculations. Computer vision is known to be one of the fields with more penetration of these new techniques. This paper explores the advantages of using GPGPU implementation to speedup a genetic algorithm used for stereo refinement. The main contribution of this paper is analyzing which genetic operators take advantage of a parallel approach and the description of an efficient state- of-the-art implementation for each one. As a result, speed-ups close to x80 can be achieved, demonstrating to be the only way of achieving close to real-time performance.es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)es_ES
dc.relation.ispartofseries;vol. 3, nº 2
dc.relation.urihttps://www.ijimai.org/journal/node/739es_ES
dc.rightsopenAccesses_ES
dc.subjectgenetic algorithmses_ES
dc.subjectGPGPUes_ES
dc.subjectparallel processinges_ES
dc.subjectIJIMAIes_ES
dc.titleGPGPU Implementation of a Genetic Algorithm for Stereo Refinementes_ES
dc.typearticlees_ES
reunir.tag~IJIMAIes_ES
dc.identifier.doihttp://dx.doi.org/10.9781/ijimai.2015.329


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