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{Optimal Camera Parameter Selection for State Estimation with Applications in Object Recognition}
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@inproceedings{Denzler01:OCP,
   author = {J. Denzler and Christopher M. Brown and H. Niemann},
   title = {{Optimal Camera Parameter Selection for State Estimation with Applications in Object Recognition}},
   pages = {305--312},
   booktitle = {Pattern Recognition --- 23rd DAGM Symposium},
   year      = {2001},
   month     = {September},
   editor    = {B. Radig and S. Florczyk},
   address   = {Berlin},
   publisher = {Springer}

abstract ={ We give a formalism for optimal camera parameter selection for
 iterative  state estimation.  It is based on Shannon's
 information theory and we
 select the camera parameters
 that maximize the mutual information, i.e. the information 
that the captured image conveys about the true
state of the system.  The technique explicitly takes into account
the apriori probability governing the computation of mutual information.
Thus, a sequential decision process can be formed by treating the aposteriori
probability at the current time step in the decsion process as the apriori
probability for the next time step.  The convergence of the decision 
process can be proven.

We demonstrate the benefits of the approach using an active object
 recognition scenario.  The results show that th esequential decision
process outperforms a random strategy, both in the sense
of recognition rate and numbers of views necessary to return
a decision.
}

annote ={
Co-winner of the DAGM Prize for this conference.
}

}