Expandable Bayesian Networks for 3D Object Description from Multiple Views and Multiple Mode Inputs

Computing 3D object descriptions from images is an important goal of computer vision. A key problem here is the evaluation of a hypothesis based on evidence that is uncertain. There have been few efforts on applying formal reasoning methods to this problem. In multiview and multimode object description problems, reasoning is required on evidence features extracted from multiple images and nonintensity data. One challenge here is that the number of the evidence features varies at runtime because the number of images being used is not fixed and some modalities may not always be available. We introduce an augmented Bayesian network, the expandable Bayesian network (EBN), which instantiates its structure at runtime according to the structure of input. We introduce the use of hidden variables to handle correlation of evidence features across images. We show an application of an EBN to a multiview building description system. Experimental results show that the proposed method gives significant and consistent performance improvement to others.