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Discriminative learning with latent variables for cluttered indoor scene understanding

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dc.contributor.authorWang, Huayan
dc.contributor.authorGould, Stephen
dc.contributor.authorKoller, Daphne
dc.date.accessioned2015-12-13T22:28:45Z
dc.date.issued2013
dc.date.updated2016-02-24T09:21:05Z
dc.description.abstractHolistic scene understanding has attracted much attention in computer vision. One of the goals is to make use of image context to help improve traditional vision tasks, such as object detection. Due to the effect of perspective projection, a set of parall
dc.identifier.issn0001-0782
dc.identifier.urihttp://hdl.handle.net/1885/74344
dc.publisherAssociation for Computing Machinery Inc (ACM)
dc.sourceCommunications of the Association for Computing Machinery
dc.subjectKeywords: Discriminative learning; ICM algorithms; Latent variable; Number of iterations; Object Detection; Perspective projections; Scene understanding; Vanishing point; Algorithms; Three dimensional computer graphics; Inference engines
dc.titleDiscriminative learning with latent variables for cluttered indoor scene understanding
dc.typeJournal article
local.bibliographicCitation.issue4
local.bibliographicCitation.lastpage99
local.bibliographicCitation.startpage92
local.contributor.affiliationWang, Huayan, Stanford University
local.contributor.affiliationGould, Stephen, College of Engineering and Computer Science, ANU
local.contributor.affiliationKoller, Daphne, Stanford University
local.contributor.authoruidGould, Stephen, u4971180
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor080104 - Computer Vision
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
local.identifier.ariespublicationf5625xPUB4075
local.identifier.citationvolume56
local.identifier.doi10.1145/2436256.2436276
local.identifier.scopusID2-s2.0-84875707816
local.identifier.thomsonID000316673300026
local.type.statusPublished Version

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