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dc.contributor.authorSIMPSON, ROBERT GILMOUR
dc.contributor.otherFaculty of Science and Technologyen_US
dc.date.accessioned2013-09-18T11:21:41Z
dc.date.available2013-09-18T11:21:41Z
dc.date.issued1992
dc.identifierNOT AVAILABLEen_US
dc.identifier.urihttp://hdl.handle.net/10026.1/1843
dc.description.abstract

Neural network analysis is proposed and evaluated as a method of analysis of marine biological data, specifically images of plankton specimens. The quantification of the various plankton species is of great scientific importance, from modelling global climatic change to predicting the economic effects of toxic red tides. A preliminary evaluation of the neural network technique is made by the development of a back-propagation system that successfully learns to distinguish between two co-occurring morphologically similar species from the North Atlantic Ocean, namely Ceratium arcticum and C. longipes. Various techniques are developed to handle the indeterminately labelled source data, pre-process the images and successfully train the networks. An analysis of the network solutions is made, and some consideration given to how the system might be extended.

en_US
dc.description.sponsorshipPlymouth Marine Laboratoryen_US
dc.language.isoenen_US
dc.publisherUniversity of Plymouthen_US
dc.titleCLASSIFICATION OF COMPLEX TWO-DIMENSIONAL IMAGES IN A PARALLEL DISTRIBUTED PROCESSING ARCHITECTUREen_US
dc.typeDoctorateen_US
plymouth.versionFull versionen_US


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