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dc.contributor.authorTabb, Ken
dc.contributor.authorDavey, N.
dc.contributor.authorGeorge, S.
dc.contributor.authorAdams, R.G.
dc.date.accessioned2008-02-05T12:12:16Z
dc.date.available2008-02-05T12:12:16Z
dc.date.issued1999
dc.identifier.citationTabb , K , Davey , N , George , S & Adams , R G 1999 , Detecting partial occlusion of humans using snakes and neural networks . in In: Procs 5th Int Conf on Engineering Applications of Neural Networks (EANN'99) . pp. 34-39 .
dc.identifier.isbn8371745125
dc.identifier.otherPURE: 85919
dc.identifier.otherPURE UUID: a79dc3f0-597d-4061-8475-09b39d478e31
dc.identifier.otherdspace: 2299/1562
dc.identifier.urihttp://hdl.handle.net/2299/1562
dc.description.abstractThis paper summarises the development of a computer system designed to detect moving humans in an image or series of images. The system combines the use of active contour models, ‘snakes’, which detect human objects in an image, with a 2 layer feedforward backpropagation neural network, to categorise the detected shape as human, or not. It was found that combining the neural network’s output values with its confidence value provided a means of classifying unseen shapes into ‘human’ and ‘non-human’. Moreover the confidence value can provide a measure of the degree of occlusion of a detected human.en
dc.language.isoeng
dc.relation.ispartofIn: Procs 5th Int Conf on Engineering Applications of Neural Networks (EANN'99)
dc.titleDetecting partial occlusion of humans using snakes and neural networksen
dc.contributor.institutionHealth and Human Sciences Central
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionCentre for Computer Science and Informatics Research
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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