Отрывок: Figure 1b shows the dependences of computational complexity on the object size w = l at a constant image size W = L = 1024 and with the same methodical charac- teristics. One can see that the computational complexity of CAM, SGI, and CEA depends weakly on object size in the frequency region, and that for the CEA it is approxi- mately quadratic in the spatial region. The SGI with the MSFD requires a ...
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dc.contributor.authorMagdeev, R.-
dc.contributor.authorTashlinskii, Al.-
dc.date.accessioned2019-05-27 10:43:50-
dc.date.available2019-05-27 10:43:50-
dc.date.issued2019-04-
dc.identifierDspace\SGAU\20190524\77079ru
dc.identifier.citationMagdeev RG, Tashlinskii AG. Efficiency of object identification for binary images. Computer Optics 2019; 43(2): 277-281. DOI: 10.18287/2412-6179-2019-43-2-277-281.ru
dc.identifier.urihttps://dx.doi.org/10.18287/2412-6179-2019-43-2-277-281-
dc.identifier.urihttp://repo.ssau.ru/handle/Zhurnal-Komputernaya-optika/Efficiency-of-object-identification-for-binary-images-77079-
dc.description.abstractIn this paper, a comparative analysis of the correlation-extreme method, the method of contour analysis and the method of stochastic gradient identification in the objects identification for a binary image is carried out. The results are obtained for a situation where possible deformations of an identified object with respect to a pattern can be reduced to a similarity model, that is, the pattern and the object may differ in scale, orientation angle, shift along the base axes, and additive noise. The identification of an object is understood as the recognition of its image with an estimate of the strain parameters relative to the template.ru
dc.description.sponsorshipThis work was supported by RFBR and the government of Ulyanovsk region, project no. 16-47-732053 and the RFBR grant, project no. 18-41-730006.ru
dc.language.isoenru
dc.publisherНовая техникаru
dc.relation.ispartofseries43;2-
dc.subjectdigital imageru
dc.subjectobject recognitionru
dc.subjectpattern recognitionru
dc.subjectcorrelation-extreme algorithmru
dc.subjectstochastic gradient identificationru
dc.subjectincorrect identification probabilityru
dc.titleEfficiency of object identification for binary imagesru
dc.typeArticleru
dc.textpartFigure 1b shows the dependences of computational complexity on the object size w = l at a constant image size W = L = 1024 and with the same methodical charac- teristics. One can see that the computational complexity of CAM, SGI, and CEA depends weakly on object size in the frequency region, and that for the CEA it is approxi- mately quadratic in the spatial region. The SGI with the MSFD requires a ...-
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