Experts Fusion and Multilayer Perceptron Based on Belief Learning for Sonar Image Classification

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dc.creatorMartin, Arnaud
dc.creatorOsswald, Christophe
dc.date2008-06-12
dc.date.accessioned2026-07-07T12:19:30Z
dc.date.available2026-07-07T12:19:30Z
dc.descriptionThe sonar images provide a rapid view of the seabed in order to characterize it. However, in such as uncertain environment, real seabed is unknown and the only information we can obtain, is the interpretation of different human experts, sometimes in conflict. In this paper, we propose to manage this conflict in order to provide a robust reality for the learning step of classification algorithms. The classification is conducted by a multilayer perceptron, taking into account the uncertainty of the reality in the learning stage. The results of this seabed characterization are presented on real sonar images.
dc.descriptionInternational Conference on Information & Communication Technologies: from Theory to Applications (ICTTA), Damascus : Syrie (2008)
dc.identifierhttps://arxiv.org/abs/0806.2007
dc.identifierhttp://arxiv.org/abs/0806.2007
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/212773
dc.subjectComputer Vision and Pattern Recognition
dc.subjectArtificial Intelligence
dc.subjectI.4; I.5
dc.titleExperts Fusion and Multilayer Perceptron Based on Belief Learning for Sonar Image Classification
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