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dc.contributor.advisorLarsen, Bjørn B.nb_NO
dc.contributor.authorFykse, Egilnb_NO
dc.date.accessioned2014-12-19T13:48:52Z
dc.date.accessioned2015-12-22T11:48:41Z
dc.date.available2014-12-19T13:48:52Z
dc.date.available2015-12-22T11:48:41Z
dc.date.created2013-10-02nb_NO
dc.date.issued2013nb_NO
dc.identifier653080nb_NO
dc.identifier.urihttp://hdl.handle.net/11250/2370873
dc.description.abstractThe objective of this thesis is to compare the suitability of FPGAs, GPUs and DSPs for digital image processing applications. Normalized cross-correlation is used as a benchmark, because this algorithm includes convolution, a common operation in image processing and elsewhere. Normalized cross-correlation is a template matching algorithm that is used to locate predefined objects in a scene image. Because the throughput of DSPs is low for efficient calculation of normalized cross-correlation, the focus is on FPGAs and GPUs. An efficient FPGA implementation of direct normalized cross-correlation is created and compared against a GPU implementation from the OpenCV library. Performance, cost, development time and power consumption are evaluated for the two platforms. The performance of the GPU implementation is slightly better than the FPGA implementation, and less time is spent developing a working solution. However, the power consumption of the GPU is higher. Both solutions are viable, so the most suitable platform will depend on the specific project requirements for image size, throughput, latency, power consumption, cost and development time.nb_NO
dc.languageengnb_NO
dc.publisherInstitutt for elektronikk og telekommunikasjonnb_NO
dc.titlePerformance Comparison of GPU, DSP and FPGA implementations of image processing and computer vision algorithms in embedded systemsnb_NO
dc.typeMaster thesisnb_NO
dc.source.pagenumber74nb_NO
dc.contributor.departmentNorges teknisk-naturvitenskapelige universitet, Fakultet for informasjonsteknologi, matematikk og elektroteknikk, Institutt for elektronikk og telekommunikasjonnb_NO


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