Full metadata record
DC FieldValueLanguage
dc.contributor.authorValero, José
dc.contributor.editorSkala, Václav
dc.date.accessioned2020-07-27T08:51:44Z
dc.date.available2020-07-27T08:51:44Z
dc.date.issued2020
dc.identifier.citationWSCG 2020: full papers proceedings: 28th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 19-28.en
dc.identifier.isbn978-80-86943-35-0
dc.identifier.issn2464–4617 (print)
dc.identifier.issn2464–4625 (CD-ROM)
dc.identifier.urihttp://wscg.zcu.cz/WSCG2020/2020-CSRN-3001.pdf
dc.identifier.urihttp://hdl.handle.net/11025/38448
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesWSCG 2020: full papers proceedingsen
dc.rights© Václav Skala - UNION Agencycs
dc.subjectkartézský komplexcs
dc.subjectdigitální obrázekcs
dc.subjectfiltrovánícs
dc.subjectNVIDIAcs
dc.subjectOpenCLcs
dc.titleInter-Pixel Filtrering of Digital Images with CUDA from NVIDIAen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThe process of filtering digital images represented by complex Cartesian allows to use the available onedimensional (1D) elements (interpixel); however, having those additional 1D elements increases both the volume of data and the time for processing them. The time reduction strategy based on a parallel computing scheme on the number of available central processing units (CPUs) does not consider additional computing resources such as those offered by general purpose graphics processing units (GPUs) of NVIDIA. Parallel computing possibilities provided by the NVIDIA GPUs were explored and, based on them, a computational scheme for the digital image Cartesian complexes filtering task was proposed using the application program interface Open Computing Language (OpenCL) provided for NVIDIA corporation GPUs. The results assessment was established by comparing the response times of the proposed solution compared to those obtained using only CPU resources. The obtained implementation is an alternative to parallelization of the filtering task, which provides response times up to 14 times faster than those obtained with the implementation that uses only the CPU resource. The NVIDIA multicore GPU significantly improves the parallelism, which can be used in conjunction with the available multicore CPU computing capacity, balancing the workload between these two computing powers using both simultaneously.en
dc.subject.translatedcartesian complexen
dc.subject.translateddigital imageen
dc.subject.translatedfilteringen
dc.subject.translatedNVIDIAen
dc.subject.translatedOpenCLen
dc.identifier.doihttps://doi.org/10.24132/CSRN.2020.3001.3
dc.type.statusPeer-revieweden
Appears in Collections:WSCG 2020: Full Papers Proceedings

Files in This Item:
File Description SizeFormat 
E47.pdfPlný text1,98 MBAdobe PDFView/Open
Show simple item record


Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/38448

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

search
navigation
  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
  3. Sborníky ZČU / UWB Proceedings
  4. WSCG: Full Papers
  5. WSCG 2020: Full Papers Proceedings