Отрывок: The training set was composed of 200 samples of 256×256 pixels, containing 100 images of each class. The images under testing had similar parameters, with the samples containing 100 images in each class. Based on this data, image classification was done. Parallel implementation of the method to generate informative areas The analysis of the algorithm structure showed that the first stage of areas computing has the maximum computa- tional complexity. As mentio...
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dc.contributor.authorKravtsova, N.S.-
dc.contributor.authorParinger, R.A.-
dc.date.accessioned2017-10-25 12:25:16-
dc.date.available2017-10-25 12:25:16-
dc.date.issued2017-08-
dc.identifierDspace\SGAU\20171020\65771ru
dc.identifier.citationKravtsova NS, Paringer RA, Kupriyanov AV. Parallel implementation of the informative areas generation method in the spatial spectrum domain. Computer Optics 2017; 41(4): 585-587.ru
dc.identifier.urihttps://dx.doi.org/10.18287/2412-6179-2017-41-4-585-587-
dc.identifier.urihttp://repo.ssau.ru/handle/Zhurnal-Komputernaya-optika/Parallel-implementation-of-the-informative-areas-generation-method-in-the-spatial-spectrum-domain-65771-
dc.description.abstractThis paper proposes a parallel implementation of the image informative segments extraction method. The images are segmented in the spatial spectrum domain. The median energy in each selected segment is viewed upon as an area. For purposes of time savings, a parallel implementation of the algorithm for calculating the areas is developed. The developed approach to the parallel algorithm implementation is tested on a high performance multicore computing system. The experiments have shown that the parallel implementation of the method allows us to obtain a three-fold speedup, which is a good result.ru
dc.description.sponsorshipThis work was partially supported by the Ministry of Education and Science of the Russian Federation under the Program of increasing SSAU's competitiveness among the world’s leading scientific and educational centers in the years 2013-2020; the Russian Foundation for Basic Research grants (# 15-29- 03823, # 15-29- 07077, # 16-41- 630761; # 16-29- 11698, # 17-01-00972); and the ONIT RAS program # 6 “Bioinformatics, modern information technologies and mathematical methods in medicine” 2017.ru
dc.language.isoenru
dc.publisherСамарский университетru
dc.relation.ispartofseries41;4-
dc.subjectdiagnostic crystallogramru
dc.subjectspatial spectrumru
dc.subjectdiscriminant analysisru
dc.subjectk-NN classificationru
dc.subjectparallel implementationru
dc.titleParallel implementation of the informative areas generation method in the spatial spectrum domainru
dc.typeArticleru
dc.textpartThe training set was composed of 200 samples of 256×256 pixels, containing 100 images of each class. The images under testing had similar parameters, with the samples containing 100 images in each class. Based on this data, image classification was done. Parallel implementation of the method to generate informative areas The analysis of the algorithm structure showed that the first stage of areas computing has the maximum computa- tional complexity. As mentio...-
dc.classindex.scsti29.31.15-
dc.classindex.scsti29.33.43-
dc.classindex.scsti20.53.23-
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