The Use of Bayesian Networks
for Structured 3-D Object Descriptions
Abstract
Computing 3-D object level descriptions from images is a key goal of
computer vision. Hypothesize and verify paradigm is a common approach
to accomplish this goal. A key problem here is evaluation of a
hypothesis
based on evidence that is uncertain. There have been few efforts on
applying formal reasoning methods on this problem. We propose a use of
Bayesian network on this problem and show an experiment with a
multi-view
building detection system. There have been few efforts to apply a formal
reasoning method in the area. Since the standard Bayesian network does
not support for evidence variables of an arbitrary number, we propose a
dynamic Bayesian network (DBN) which dynamically instanciates its
structure according to the input evidence. The structure of the Bayesian
network is designed by performing a correlation analysis, which brings
a new concept of using hidden nodes in Bayesian network. Experimental
results show that the proposed method gives good results.
On-line references