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Automatic Arm Region Segmentation and Background Image Composition

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dc.contributor.author김동현-
dc.contributor.author박세훈-
dc.contributor.author서영건-
dc.date.accessioned2022-12-26T19:16:25Z-
dc.date.available2022-12-26T19:16:25Z-
dc.date.issued2017-
dc.identifier.issn1598-2009-
dc.identifier.issn2287-738X-
dc.identifier.urihttps://scholarworks.gnu.ac.kr/handle/sw.gnu/14286-
dc.description.abstractIn first-person perspective training system, the users needs realistic experience. For providing this experience, the system should offer the users virtual and real images at the same time. We propose an automatic a persons’s arm segmentation and image composition method. It consists of arm segmentation part and image composition part. Arm segmentation uses an arbitrary image as input and outputs arm segment or alpha matte. It enables end-to-end learning because we make use of FCN in this part. Image composition part conducts image combination between the result of arm segmentation and other image like road, building, etc. To train the network in arm segmentation, we used arm images through dividing the videos that we took ourselves for the training data.-
dc.format.extent8-
dc.language영어-
dc.language.isoENG-
dc.publisher한국디지털콘텐츠학회-
dc.titleAutomatic Arm Region Segmentation and Background Image Composition-
dc.title.alternativeAutomatic Arm Region Segmentation and Background Image Composition-
dc.typeArticle-
dc.publisher.location대한민국-
dc.identifier.doi10.9728/dcs.2017.18.8.1509-
dc.identifier.bibliographicCitation디지털콘텐츠학회논문지, v.18, no.8, pp 1509 - 1516-
dc.citation.title디지털콘텐츠학회논문지-
dc.citation.volume18-
dc.citation.number8-
dc.citation.startPage1509-
dc.citation.endPage1516-
dc.identifier.kciidART002309628-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClasskci-
dc.subject.keywordAuthorArm region-
dc.subject.keywordAuthorArm segmentation-
dc.subject.keywordAuthorBackground composition-
dc.subject.keywordAuthorFCN-
dc.subject.keywordAuthor팔 영역-
dc.subject.keywordAuthor팔 분할-
dc.subject.keywordAuthor배경 합성-
dc.subject.keywordAuthorFCN-
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