| Type de publication : |
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Articles dans des revues avec comité de lecture |
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| Domaine : |
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Informatique/Traitement des images
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| Titre : |
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Mixed-state causal modeling for statistical KL-based motion texture tracking |
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| Auteur(s) : |
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Tomas Crivelli 1, 2, Bruno Cernuschi-Frias ( ) 2, Patrick Bouthemy () 1, Jian-Feng Yao () 1, 3 |
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| Projet(s) / laboratoire(s) : |
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| Résumé : |
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We are interested in the modeling and tracking of dynamic or motion textures, which refer to dynamic contents that can be classified as a texture with motion (fire, smoke, crowd of people). Experimentally we observe that they depict motion maps with values of a mixed type: a discrete value at zero (absence of motion) and continuous non-null motion values. We thus introduce a temporal mixed-state Markov model for the characterization of motion textures from which a set of 13 parameters is extracted as the descriptive feature of the dynamic content. Then, a motion texture tracking strategy is proposed using the conditional Kullback–Leibler (KL) divergence between mixed-state probability densities, which allows us to estimate the position using a statistical matching approach. |
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| Langue du document : |
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Anglais |
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| Titre de la revue : |
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| Pattern Recognition Letters |
| Publisher |
Elsevier |
| ISSN |
0167-8655 |
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| Date de publication : |
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15/10/2010 |
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| Audience : |
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internationale |
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| Editeur commercial : |
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Elsevier |
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| Volume : |
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31 |
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| Numéro : |
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14 |
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| Pagination : |
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2286-2294 |
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| Texte intégral éditeur (url) : |
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http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V15-50H7D42-1&_user=6068168&_coverDate=10%2F15%2F2010&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_searchStrId=1561397025&_rerunOrigin=google&_acct=C000016487&_version |
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