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dc.contributor.authorZhu, Song Chun
dc.contributor.authorMumford, David Bryant
dc.date.accessioned2010-02-04T19:50:53Z
dc.date.issued1997
dc.identifier.citationZhu, Song Chun, and David Bryant Mumford. 1997. Learning generic prior models for visual computation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition: June 17 - 19, San Juan. Puerto Rico, ed. IEEE Computer Society, 463-469. Los Alamitos, CA : IEEE Computer Society.en_US
dc.identifier.isbn0818678224en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:3627119
dc.description.abstractThis paper presents a novel theory for learning generic prior models from a set of observed natural images based on a minimax entropy theory that the authors studied in modeling textures. We start by studying the statistics of natural images including the scale invariant properties, then generic prior models were learnt to duplicate the observed statistics. The learned Gibbs distributions confirm and improve the forms of existing prior models. More interestingly inverted potentials are found to be necessary, and such potentials form patterns and enhance preferred image features. The learned model is compared with existing prior models in experiments of image restoration.en_US
dc.description.sponsorshipMathematicsen_US
dc.language.isoen_USen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.isversionofdoi:10.1109/CVPR.1997.609366en_US
dc.relation.hasversionhttp://www.dam.brown.edu/people/mumford/Papers/DigitizedVisionPapers--forNonCommercialUse/97a--LearningPriors-Zhu.pdfen_US
dash.licenseLAA
dc.titleLearning Generic Prior Models for Visual Computationen_US
dc.typeMonograph or Booken_US
dc.description.versionVersion of Recorden_US
dash.depositing.authorMumford, David Bryant
dc.date.available2010-02-04T19:50:53Z
dc.identifier.doi10.1109/CVPR.1997.609366*
dash.contributor.affiliatedMumford, David


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