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dc.contributor.author Onyango, Christine M.
dc.contributor.author Marchant, John A.
dc.contributor.author Zwiggelaar, Reyer
dc.date.accessioned 2008-03-13T10:23:53Z
dc.date.available 2008-03-13T10:23:53Z
dc.date.issued 1997-06
dc.identifier.citation Onyango , C M , Marchant , J A & Zwiggelaar , R 1997 , ' Modelling uncertainty in agricultural image analysis ' Computers and Electronics in Agriculture , vol 17 , no. 3 , pp. 295-305 . , 10.1016/S0168-1699(97)01322-7 en
dc.identifier.other PURE: 76114
dc.identifier.other dspace: 2160/535
dc.identifier.uri http://hdl.handle.net/2160/535
dc.identifier.uri http://www.ingentaconnect.com/content/els/01681699/1997/00000017/00000003/art01322 en
dc.description C.M. Onyango, J.A. Marchant and R. Zwiggelaar, 'Modelling uncertainty in agricultural image analysis', Computers and Electronics in Agriculture 17 (3), 295-305 (1997) en
dc.description.abstract No absolute certainty can be given for information derived from images. In most cases image analysis uses single algorithms, or multiple single algorithms' results which are combined in an ad hoc manner, to derive certain information (e.g. edges and textures) to segment images into various regions of interest. However, more robust methods of data fusion can be developed which are based on mathematical foundations of probability theory. One such method combines results from single algorithms using a Bayesian network. This should improve the confidence in the derived image segmentation and gives a direct measure of the probability of each region to be classified correctly. Specific agricultural examples using a Bayesian data fusion approach are given. en
dc.format.extent 11 en
dc.language.iso eng
dc.relation.ispartof Computers and Electronics in Agriculture en
dc.subject Bayesian networks en
dc.subject Uncertainty en
dc.subject Image analysis en
dc.subject Algorithm fusion en
dc.title Modelling uncertainty in agricultural image analysis en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1016/S0168-1699(97)01322-7
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Vision, Graphics and Visualisation Group en
dc.description.status Peer reviewed en


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