  In this paper, an Entropy functional based online Adaptive Decision Fusion
(EADF) framework is developed for image analysis and computer vision
applications. In this framework, it is assumed that the compound algorithm
consists of several sub-algorithms each of which yielding its own decision as a
real number centered around zero, representing the confidence level of that
particular sub-algorithm. Decision values are linearly combined with weights
which are updated online according to an active fusion method based on
performing entropic projections onto convex sets describing sub-algorithms. It
is assumed that there is an oracle, who is usually a human operator, providing
feedback to the decision fusion method. A video based wildfire detection system
is developed to evaluate the performance of the algorithm in handling the
problems where data arrives sequentially. In this case, the oracle is the
security guard of the forest lookout tower verifying the decision of the
combined algorithm. Simulation results are presented. The EADF framework is
also tested with a standard dataset.
