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dc.contributor.authorCree, Michael J.en_NZ
dc.coverage.spatialAuckland, New Zealanden_NZ
dc.date.accessioned2015-12-03T01:49:12Z
dc.date.available2015en_NZ
dc.date.available2015-12-03T01:49:12Z
dc.date.issued2015en_NZ
dc.identifier.citationCree, M. J. (2015). Vectorised SIMD Implementations of Morphology Algorithms. In Proceedings of the Image and Vision Computing New Zealand, 23-24 November 2015, Auckland, New Zealand.en
dc.identifier.urihttps://hdl.handle.net/10289/9785
dc.description.abstractWe explore vectorised implementations, exploiting single instruction multiple data (SIMD) CPU instructions on commonly used architectures, of three efficient algorithms for morphological dilation and erosion. We discuss issues specific to SIMD implementation and describe how they guide algorithm choice. We compare our implementations to a commonly used opensource SIMD accelerated machine vision library and find orders of magnitude speed-ups can be achieved for erosions using two-dimensional structuring elements.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.rightsThis is an author’s accepted version of an paper published in the proceedings of Image and Vision Computing New Zealand 2015. ©2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
dc.sourceImage and Vision Computing New Zealand 2015en_NZ
dc.titleVectorised SIMD Implementations of Morphology Algorithmsen_NZ
dc.typeConference Contribution
pubs.begin-page1
pubs.elements-id134175
pubs.end-page6
pubs.finish-date2015-11-24en_NZ
pubs.notesWe explore vectorised implementations, exploiting single instruction multiple data (SIMD) CPU instructions on commonly used architectures, of three efficient algorithms for morphological dilation and erosion. We discuss issues specific to SIMD implementation and describe how they guide algorithm choice. We compare our implementations to a commonly used opensource SIMD accelerated machine vision library and find orders of magnitude speed-ups can be achieved for erosions using two-dimensional structuring elements.en_NZ
pubs.organisational-group/Waikato
pubs.organisational-group/Waikato/FSEN
pubs.organisational-group/Waikato/FSEN/Engineering
pubs.publisher-urlhttps://ivcnz2015.aut.ac.nz/en_NZ
pubs.start-date2015-11-23en_NZ


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