1 November 1992 Accurate estimation of surface properties by integrating information using Bayesian networks
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Proceedings Volume 1828, Sensor Fusion V; (1992) https://doi.org/10.1117/12.131655
Event: Applications in Optical Science and Engineering, 1992, Boston, MA, United States
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.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Simon J. Davies, Simon J. Davies, A. David Marshall, A. David Marshall, Ralph R. Martin, Ralph R. Martin, } "Accurate estimation of surface properties by integrating information using Bayesian networks", Proc. SPIE 1828, Sensor Fusion V, (1 November 1992); doi: 10.1117/12.131655; https://doi.org/10.1117/12.131655

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