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dc.contributor.authorSedding, Helmuten_US
dc.contributor.authorDeger, Ferdinanden_US
dc.contributor.authorDammertz, Holgeren_US
dc.contributor.authorBouecke, Janen_US
dc.contributor.authorLensch, Hendrik P. A.en_US
dc.contributor.editorReinhard Koch and Andreas Kolb and Christof Rezk-Salamaen_US
dc.description.abstractWe present a massively parallel object recognition system based on a cortex-like structure. Due to its nature, this general, biologically motivated system can be parallelized efficiently on recent many-core graphics processing units (GPU). By implementing the entire pipeline on the GPU, by rigorously optimizing memory bandwidth and by minimizing branch divergence, we achieve significant speedup compared to both recent CPU as well as GPU implementations for reasonably sized feature dictionaries. We demonstrate an interactive application even on a less powerful laptop which is able to classify webcam images and to learn novel categories in real time.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.4.8 [Image Processing and Computer Vision]: Scene Analysis, Subject: Object recognitionen_US
dc.titleMassively Parallel Multiclass Object Recognitionen_US
dc.description.seriesinformationVision, Modeling, and Visualization (2010)en_US

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  • VMV10
    ISBN 978-3-905673-79-1

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