Fusing multiple sources with Bayesian networks to achieve accurate object descriptions

Simon J. Davies; A. David Marshall; Ralph R. Martin

doi:10.1117/12.220947

15 September 1995 Fusing multiple sources with Bayesian networks to achieve accurate object descriptions

Simon J. Davies, A. David Marshall, Ralph R. Martin

Proceedings Volume 2589, Sensor Fusion and Networked Robotics VIII; (1995) https://doi.org/10.1117/12.220947
Event: Photonics East '95, 1995, Philadelphia, PA, United States

Abstract

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.

Citation Download Citation

Simon J. Davies, A. David Marshall, and Ralph R. Martin "Fusing multiple sources with Bayesian networks to achieve accurate object descriptions", Proc. SPIE 2589, Sensor Fusion and Networked Robotics VIII, (15 September 1995); https://doi.org/10.1117/12.220947

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KEYWORDS

Image segmentation

Image processing

Data processing

Data fusion

Matrices

Image fusion

Data modeling

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