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Optimal Selection of Camera Parameters for State Estimation of Static Systems: An Information Theoretic Approach
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@TechReport{Denzler:TR00,
  author = 	 {Joachim Denzler and Christopher M. Brown},
  title = 	 {Optimal Selection of Camera Parameters for State Estimation of Static Systems: An Information Theoretic Approach},
  institution =  {CS Dept University of Rochester},
  year = 	 {2000}
  number = 	 {732},
  address = 	 {Rochester NY},
  month = 	 {August},
  abstract =  {
In this paper we introduce a formalism for optimal sensor parameter selection for iterative state estimation in static systems. 
In contrast to common approaches, where a certain metric---for example, the mean squared error between true and estimated state
---is optimized during state estimation, in this work the optimality is defined in terms of reduction in uncertainty in the state
 estimation process. The main assumption is that state estimation becomes more reliable if the uncertainty and ambiguity in the state 
estimation process can be reduced.

We consider a framework based on Shannon's information theory and select the camera parameters that maximize the mutual information, 
i.e., optimize the information that the captured image conveys about the true state of the system. The technique implicitly takes into 
account the a priori probabilities governing the computation of the mutual information. Thus a sequential decision process can be 
formed by treating the a priori probability at a certain time step in the decision process as the a posteriori probability of the 
previous time step.

We demonstrate the benefits of our approach using an object recognition scenario and an active pan/tilt/zoom camera. During the 
sequential decision process the camera looks to parts of the object that allow the most reliable distinction of similar looking objects. 
We performed experiments with discrete density representation as well as with continuous densities and Monte Carlo evaluation of the 
mutual information. The results show that the sequential decision process outperforms a random gaze control, both in the sense of 
recognition rate and number of views necessary to return a decision.}

}