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dc.contributor.authorAlexey, Abramov
dc.contributor.authorKulvicius, Tomas
dc.contributor.authorWörgötter, Florentin
dc.contributor.authorDellen, Babette
dc.contributor.otherInstitut de Robòtica i Informàtica Industrial
dc.description.abstractEfficient segmentation of color images is important for many applications in computer vision. Non-parametric solutions are required in situations where little or no prior knowledge about the data is available. In this paper, we present a novel parallel image segmentation algorithm which segments images in real-time in a non-parametric way. The algorithm finds the equilibrium states of a Potts model in the superparamagnetic phase of the system. Our method maps perfectly onto the Graphics Processing Unit (GPU) architecture and has been implemented using the framework NVIDIA Compute Unified Device Architecture (CUDA). For images of 256 X 320 pixels we obtained a frame rate of 30 Hz that demonstrates the applicability of the algorithm to video-processing tasks in real-time
dc.format.extent13 p.
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Reconeixement de formes
dc.subject.lcshPattern recognition systems
dc.titleReal-time image segmentation on a GPU
dc.typeConference report
dc.subject.lemacReconeixement de formes (Informàtica)
dc.subject.inspecClassificació INSPEC::Pattern recognition
dc.rights.accessOpen Access

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