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dc.date.accessioned2017-05-12T14:26:31Z
dc.date.available2017-05-12T14:26:31Z
dc.date.issued12/9/2014
dc.identifier.urihttp://hdl.handle.net/1853/56946
dc.description.abstractA method for providing improved performance in retrieving and classifying causal sets of events from an unstructured signal can comprise applying a temporal-causal analysis to the unstructured signal. The temporal-causal analysis can comprise representing the occurrence times of visual events from an unstructured signal as a set of point processes. An exemplary embodiment can comprise interpreting a set of visual codewords produced by a space-time-dictionary representation of the unstructured video sequence as the set of point processes. A nonparametric estimate of the cross-spectrum between pairs of point processes can be obtained. In an exemplary embodiment, a spectral version of the pairwise test for Granger causality can be applied to the nonparametric estimate to identify patterns of interactions between visual codewords and group them into semantically meaningful independent causal sets. The method can further comprise leveraging the segmentation achieved during temporal causal analysis to improve performance in categorizing causal sets.
dc.titleSystems And Methods For Retrieving Causal Sets Of Events From Unstructured Signals
dc.typePatent
dc.contributor.patentcreatorRehg, James M.
dc.contributor.patentcreatorPrabhakar, Karthir
dc.contributor.patentcreatorOh, Sangmin
dc.contributor.patentcreatorWang, Ping
dc.contributor.patentcreatorAbowd, Gregory D.
dc.identifier.patentnumber8909025
dc.description.assigneeGeorgia Tech Research Corporation
dc.identifier.patentapplicationnumber13/427610
dc.date.filed3/22/2012
dc.identifier.uspc386/241
dc.identifier.cpcG06K9/00718


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