Informative views and sequential recognition

  • Tal Arbel
  • Frank P. Ferrie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1064)


In this paper we introduce a method for distinguishing between informative and uninformative viewpoints as they pertain to an active observer seeking to identify an object in a known environment. The method is based on a generalized inverse theory using a probabilistic framework where assertions are represented by conditional probability density functions. Experimental results are presented showing how the resulting algorithms can be used to distinguish between informative and uninformative viewpoints, rank a sequence of images on the basis of their information (e.g. to generate a set of characteristic views), and sequentially identify an unknown object.


Probability Density Function Unknown Object Ambiguous Case Clear Winner Alarm Clock 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Tal Arbel
    • 1
  • Frank P. Ferrie
    • 1
  1. 1.Centre for Intelligent MachinesMcGill UniversityMontréalCanada

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