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dc.contributor.author Kim, Hyun Duk -
dc.contributor.author Lee, Sang Heon -
dc.contributor.author Sohn, Myoung Kyu -
dc.contributor.author Kim, Dong Ju -
dc.date.accessioned 2018-01-25T01:12:31Z -
dc.date.available 2018-01-25T01:12:31Z -
dc.date.created 2017-04-20 -
dc.date.issued 2014-12 -
dc.identifier.issn 2192-1962 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/5277 -
dc.description.abstract In this paper, a novel approach for head pose estimation in gray-level images is presented. In the proposed algorithm, two techniques were employed. In order to deal with the large set of training data, the method of Random Forests was employed; this is a state-of-the-art classification algorithm in the field of computer vision. In order to make this system robust in terms of illumination, a Binary Pattern Run Length matrix was employed; this matrix is combination of Binary Pattern and a Run Length matrix. The binary pattern was calculated by randomly selected operator. In order to extract feature of training patch, we calculate statistical texture features from the Binary Pattern Run Length matrix. Moreover we perform some techniques to real-time operation, such as control the number of binary test. Experimental results show that our algorithm is efficient and robust against illumination change. © 2014, Kim et al.; licensee Springer. -
dc.language English -
dc.publisher Springer Science + Business Media -
dc.title Illumination invariant head pose estimation using random forests classifier and binary pattern run length matrix -
dc.type Article -
dc.identifier.doi 10.1186/s13673-014-0009-7 -
dc.identifier.wosid 000214788300009 -
dc.identifier.scopusid 2-s2.0-84941027724 -
dc.identifier.bibliographicCitation Human-centric Computing and Information Sciences, v.4, no.1, pp.1 - 12 -
dc.description.isOpenAccess TRUE -
dc.subject.keywordAuthor Head pose estimation -
dc.subject.keywordAuthor Random forests -
dc.subject.keywordAuthor Binary pattern -
dc.subject.keywordAuthor Run Length matrix -
dc.subject.keywordAuthor Illumination-invariant -
dc.citation.endPage 12 -
dc.citation.number 1 -
dc.citation.startPage 1 -
dc.citation.title Human-centric Computing and Information Sciences -
dc.citation.volume 4 -
dc.description.journalRegisteredClass scopus -
dc.relation.journalResearchArea Computer Science -
dc.relation.journalWebOfScienceCategory Computer Science, Information Systems -
dc.type.docType Article -
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Division of Automotive Technology 1. Journal Articles

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