%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{gatica02b-conf,
         author = {Gatica-Perez, Daniel and Sun, Ming-Ting},
       projects = {Idiap},
          title = {Linking Objects in Videos by Importance Sampling},
      booktitle = {IEEE International Conference on Multimedia and Expo},
           year = {2002},
       crossref = {gatica02b},
       abstract = {We present an approach to create hyper-links between video segments that contain objects of interest, based on video structuring, object definition, and stochastic object localization in the video structure. Localization is formulated in the Metric Mixture model framework, which allows for the joint probabilistic modeling of a (user-defined) set of color appearance exemplars and their geometric transformations. Candidate object configurations are drawn from a prior distribution using importance sampling -which guides the search towards regions of the configuration space likely to contain the correct object configuration, thus avoiding exhaustive processing- and evaluated using Bayes' rule. Results of linking real objects (with changes of size and pose) in several home videos illustrate the performance of the method.},
            pdf = {https://publications.idiap.ch/attachments/reports/2002/rr02-20.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2002/rr02-20.ps.gz},
ipdmembership={vision},
}



crossreferenced publications: 
@TECHREPORT{gatica02b,
         author = {Gatica-Perez, Daniel and Sun, Ming-Ting},
       projects = {Idiap},
          title = {Linking Objects in Videos by Importance Sampling},
           type = {Idiap-RR},
         number = {Idiap-RR-20-2002},
           year = {2002},
    institution = {IDIAP},
       abstract = {We present an approach to create hyper-links between video segments that contain objects of interest, based on video structuring, object definition, and stochastic object localization in the video structure. Localization is formulated in the Metric Mixture model framework, which allows for the joint probabilistic modeling of a (user-defined) set of color appearance exemplars and their geometric transformations. Candidate object configurations are drawn from a prior distribution using importance sampling -which guides the search towards regions of the configuration space likely to contain the correct object configuration, thus avoiding exhaustive processing- and evaluated using Bayes' rule. Results of linking real objects (with changes of size and pose) in several home videos illustrate the performance of the method.},
            pdf = {https://publications.idiap.ch/attachments/reports/2002/rr02-20.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2002/rr02-20.ps.gz},
ipdmembership={vision},
}