Fusing multiple sources with Bayesian networks to achieve accurate object descriptions
Simon J. Davies, A. David Marshall, Ralph Robert Martin · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
This paper introduces an approach that details how data from a variety of different sources can be combined to produce more reliable and accurate segmentation. By this we mean a surface estimation consisting of surface properties (e.g. orientation, curvature, etc.) and a precise boundary of the surface. Information from more than one source can be useful in that we can use data from one source to overcome a deficiency in another source. These concepts can be extended here to include more sources of data including shape from shading and passive stereo techniques to give us further information. Bayesian networks are used to process the variety of data that is available in order to provide the best segmentation results by extracting the most valuable information from the source images by assessing the plausibility of hypotheses made about the object's surfaces and their interaction. Other papers have dealt with the construction and defining of the Bayesian network whereas this paper will deal in more depth with the reasoning process when new information is incorporated into the network and also it's performance in the segmentation process.