@inproceedings{ ISI:000265921400372,
Author = {Ofli, F. and Erzin, E. and Yemez, Y. and Tekalp, A. M. and Erdem, C. E.
   and Erdem, A. T. and Abaci, T. and Ozkan, M. K.},
Book-Group-Author = {{IEEE}},
Title = {{UNSUPERVISED DANCE FIGURE ANALYSIS FROM VIDEO FOR DANCING AVATAR
   ANIMATION}},
Booktitle = {{2008 15TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-5}},
Series = {{IEEE International Conference on Image Processing (ICIP)}},
Year = {{2008}},
Pages = {{1484-1487}},
Note = {{15th IEEE International Conference on Image Processing (ICIP 2008), San
   Diego, CA, OCT 12-15, 2008}},
Organization = {{IEEE Signal Proc Soc}},
Abstract = {{This paper presents a framework for unsupervised video analysis in the
   context of dance performances, where gestures and 3D movements of a
   dancer are characterized by repetition of a set of unknown dance
   figures. The system is trained in an unsupervised manner using Hidden
   Markov Models (HMMs) to automatically segment multi-view video
   recordings of a dancer into recurring elementary temporal body motion
   patterns to identify the dance figures. That is, a parallel HMM
   structure is employed to automatically determine the number and the
   temporal boundaries of different dance figures in a given dance video.
   The success of the analysis framework has been evaluated by visualizing
   these dance figures on a dancing avatar animated by the computed 3D
   analysis parameters. Experimental results demonstrate that the proposed
   framework enables synthetic agents and/or robots to learn dance figures
   from video automatically.}},
DOI = {{10.1109/ICIP.2008.4712047}},
ISSN = {{1522-4880}},
ISBN = {{978-1-4244-1765-0}},
ResearcherID-Numbers = {{Erzin, Engin/H-1716-2011
   Eroglu Erdem, Cigdem/J-4216-2012}},
ORCID-Numbers = {{Erzin, Engin/0000-0002-2715-2368
   Eroglu Erdem, Cigdem/0000-0002-9264-5652}},
Unique-ID = {{ISI:000265921400372}},
}

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K."],"year":2008,"bibtype":"inproceedings","biburl":"http://home.ku.edu.tr/~eerzin/pubs/mvgl.bib","bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"propositions":[],"lastnames":["Ofli"],"firstnames":["F."],"suffixes":[]},{"propositions":[],"lastnames":["Erzin"],"firstnames":["E."],"suffixes":[]},{"propositions":[],"lastnames":["Yemez"],"firstnames":["Y."],"suffixes":[]},{"propositions":[],"lastnames":["Tekalp"],"firstnames":["A.","M."],"suffixes":[]},{"propositions":[],"lastnames":["Erdem"],"firstnames":["C.","E."],"suffixes":[]},{"propositions":[],"lastnames":["Erdem"],"firstnames":["A.","T."],"suffixes":[]},{"propositions":[],"lastnames":["Abaci"],"firstnames":["T."],"suffixes":[]},{"propositions":[],"lastnames":["Ozkan"],"firstnames":["M.","K."],"suffixes":[]}],"book-group-author":"IEEE","title":"UNSUPERVISED DANCE FIGURE ANALYSIS FROM VIDEO FOR DANCING AVATAR ANIMATION","booktitle":"2008 15TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-5","series":"IEEE International Conference on Image Processing (ICIP)","year":"2008","pages":"1484-1487","note":"15th IEEE International Conference on Image Processing (ICIP 2008), San Diego, CA, OCT 12-15, 2008","organization":"IEEE Signal Proc Soc","abstract":"This paper presents a framework for unsupervised video analysis in the context of dance performances, where gestures and 3D movements of a dancer are characterized by repetition of a set of unknown dance figures. 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K.},\nBook-Group-Author = {{IEEE}},\nTitle = {{UNSUPERVISED DANCE FIGURE ANALYSIS FROM VIDEO FOR DANCING AVATAR\n ANIMATION}},\nBooktitle = {{2008 15TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-5}},\nSeries = {{IEEE International Conference on Image Processing (ICIP)}},\nYear = {{2008}},\nPages = {{1484-1487}},\nNote = {{15th IEEE International Conference on Image Processing (ICIP 2008), San\n Diego, CA, OCT 12-15, 2008}},\nOrganization = {{IEEE Signal Proc Soc}},\nAbstract = {{This paper presents a framework for unsupervised video analysis in the\n context of dance performances, where gestures and 3D movements of a\n dancer are characterized by repetition of a set of unknown dance\n figures. The system is trained in an unsupervised manner using Hidden\n Markov Models (HMMs) to automatically segment multi-view video\n recordings of a dancer into recurring elementary temporal body motion\n patterns to identify the dance figures. 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