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DC poleHodnotaJazyk
dc.contributor.authorSah, Sudkahar
dc.contributor.authorVaněk, Jan
dc.contributor.authorRoh, YoungJun
dc.contributor.authorWasnik, Ratul
dc.date.accessioned2015-12-14T06:35:08Z
dc.date.available2015-12-14T06:35:08Z
dc.date.issued2012
dc.identifier.citationSAD, Sudkahar; VANĚK, Jan; ROH, YoungJun; WASNIK, Ratul. GPU accelerated real time rotation, scale and translation invariant image registration method. In: International conference on image analysis and recognition. Berlin: Springer, 2012, p. 224-233. (Lecture notes in computer science; 7324). ISBN 978-3-642-31294-6.cs
dc.identifier.isbn978-3-642-31294-6
dc.identifier.urihttp://www.kky.zcu.cz/cs/publications/SudhakarSah_2012_GPUAcceleratedReal
dc.identifier.urihttp://hdl.handle.net/11025/16979
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherSpringeren
dc.relation.ispartofseriesLecture notes in computer science; 7324en
dc.rights© Sudhakar Sah - Jan Vaněk - YoungJun Roh - Ratul Wasnikcs
dc.subjectGPUcs
dc.subjectregistrace obrazucs
dc.subjectCUDAcs
dc.subjectOpenCLcs
dc.subjectrozpoznávání objektůcs
dc.titleGPU accelerated real time rotation, scale and translation invariant image registration methoden
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThis paper presents highly optimized implementation of image registration method that is invariant to rotation scale and translation. Image registration method using FFT works with comparable accuracy as similar methods proposed in the literature, but practical applications seldom use this technique because of high computational requirement. However, this method is highly parallelizable and offloading it to the commodity graphics cards increases its performance drastically. We are proposing the parallel implementation of FFT based registration method on CUDA and OpenCL. Performance analysis of this implementation suggests that the parallel version can be used for real time image registration even for image size up to 2k x 2k. We have achieved significant speed up of up to 345x on NVIDIA GTX 580 using CUDA and up to 116x on AMD HD 6950 using OpenCL. Comparison of our implementation with other GPU based registration method reveals that our implementation performs better compared to other implementations.en
dc.subject.translatedGPUen
dc.subject.translatedimage registrationen
dc.subject.translatedCUDAen
dc.subject.translatedOpenCLen
dc.subject.translatedobject recognitionen
dc.identifier.doi10.1007/978-3-642-31295-3_27
dc.type.statusPeer-revieweden
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  2. Publikační činnost / Publications
  3. Fakulta aplikovaných věd / Faculty of Applied Sciences
  4. Katedra informatiky a výpočetní techniky / Department of Computer Science and Engineering
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