Principal Component Analysis with Missing Data and Its Application to Polyhedral Object Modeling.
Resource URI: https://dblp.l3s.de/d2r/resource/publications/journals/pami/ShumIR95
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dcterms:
bibliographicCitation
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http://dblp.uni-trier.de/rec/bibtex/journals/pami/ShumIR95
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dc:
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https://dblp.l3s.de/d2r/resource/authors/Heung-Yeung_Shum
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dc:
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https://dblp.l3s.de/d2r/resource/authors/Katsushi_Ikeuchi
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dc:
creator
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https://dblp.l3s.de/d2r/resource/authors/Raj_Reddy
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http://dx.doi.org/doi.org%2F10.1109%2F34.406651
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DBLP journals/pami/ShumIR95
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dcterms:
issued
1995
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swrc:
journal
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rdfs:
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Principal Component Analysis with Missing Data and Its Application to Polyhedral Object Modeling.
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https://dblp.l3s.de/d2r/resource/authors/Heung-Yeung_Shum
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https://dblp.l3s.de/d2r/resource/authors/Katsushi_Ikeuchi
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https://dblp.l3s.de/d2r/resource/authors/Raj_Reddy
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swrc:
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9
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swrc:
pages
854-867
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rdfs:
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dc:
subject
Computer vision, 3D object modeling, multiple view merging, range image processing, principal component analysis.
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dc:
title
Principal Component Analysis with Missing Data and Its Application to Polyhedral Object Modeling.
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17
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