Bayesian network using edge probabilities for target detection and recognition
Renjian Zhao, Patrick A. Kelly, Haluk Derin · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
It has been noted recently that, in a number of applications, effective approximations to complex posterior probabilities can be computed through the framework of probability propagation in Bayesian networks. In this paper, we develop a Bayesian network for the problem of target detection and recognition. Our multiply-connected Bayesian network is based on a distribution decomposition of the form p(y,t,e)=p(y|t,e)p(t|e)p(e), where y is an observed image, t is a set of target pixels together with identifying labels, and e is a set of edge pixels. Running a probability propagation algorithm on this network leads to an approximation of the desired posterior probability p(t|y) as a product of terms that correlate the conditional observation distribution p(y|t,e) and target distribution p(t|e) with a posterior edge distribution p(e|y). We describe approaches for generating the required posterior edge distribution and for calculating the correlations through template matching. The result is a computationally-efficient algorithm for computing posterior target probabilities that can be used either to generate hard decisions or for fusion with other information. Target detection based on the posterior probability p(t|y) is discussed in the paper.