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Proceedings Paper

Accurate estimation of surface properties by integrating information using Bayesian networks
Author(s): Simon J. Davies; A. David Marshall; Ralph R. Martin
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Paper Abstract

Segmenting an image of an object using a single information source (e.g. depth data, light intensity) or a single processing method (e.g. determining edges) can prove to be unreliable as each approach has its own advantages and disadvantages. However, if these sources of data or processes are combined, the advantages of each can be harnessed to given more accurate results. For example, depth data gives explicit three-dimensional geometric information while light intensities can give a more accurate edge representation than many three-dimensional sensing methods. The process of combining sources of information results in greater amounts of data needing analysis. Bayesian networks may be used to guide the segmentation process and to extract the most valuable information from each source image by assessing the plausibility of hypotheses made about the object's surfaces and their interaction. The believability of these hypotheses can then be estimated by examining the original source images and utilizing this information as complimentary or contrasting evidence.

Paper Details

Date Published: 1 November 1992
PDF: 10 pages
Proc. SPIE 1828, Sensor Fusion V, (1 November 1992); doi: 10.1117/12.131655
Show Author Affiliations
Simon J. Davies, Univ. of Wales College Cardiff (United Kingdom)
A. David Marshall, Univ. of Wales College Cardiff (United Kingdom)
Ralph R. Martin, Univ. of Wales College Cardiff (United Kingdom)

Published in SPIE Proceedings Vol. 1828:
Sensor Fusion V
Paul S. Schenker, Editor(s)

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