Please use this identifier to cite or link to this item: https://doi.org/10.1109/IJCNN.2013.6706892
DC FieldValue
dc.titleThe use of optical and sonar images in the human and dolphin brain for image classification
dc.contributor.authorJalali, S.
dc.contributor.authorSeekings, P.J.
dc.contributor.authorTan, C.
dc.contributor.authorRatheesh, A.
dc.contributor.authorLim, J.-H.
dc.contributor.authorTaylor, E.A.
dc.date.accessioned2014-12-12T07:36:43Z
dc.date.available2014-12-12T07:36:43Z
dc.date.issued2013
dc.identifier.citationJalali, S.,Seekings, P.J.,Tan, C.,Ratheesh, A.,Lim, J.-H.,Taylor, E.A. (2013). The use of optical and sonar images in the human and dolphin brain for image classification. Proceedings of the International Joint Conference on Neural Networks : -. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/IJCNN.2013.6706892" target="_blank">https://doi.org/10.1109/IJCNN.2013.6706892</a>
dc.identifier.isbn9781467361293
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/116131
dc.description.abstractIn this paper we propose a new biologically inspired model which simulates the visual pathways in the human brain used for classification of matching optical and sonar derived images. Marine mammals, such as dolphins, that live in waters with poor optical clarity and low light levels such as littoral zones, use a combination of optical vision and biosonar to navigate and hunt for prey. Given that dolphins have evolved a synergistic combination of optical visual input and acoustic/sonar input, the primary focus of this paper is on reaching a similar level of synergy for a diver or Autonomous Underwater Vehicle (AUV) platform equipped with a system to extend the range and resolution of vision in poor ambient visibility. We propose a biologically inspired model that combines and processes visual images acquired via optical and acoustic pathways and show that the combined model enhances the accuracy of automatic classification of target objects in underwater images. © 2013 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/IJCNN.2013.6706892
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentTROPICAL MARINE SCIENCE INSTITUTE
dc.description.doi10.1109/IJCNN.2013.6706892
dc.description.sourcetitleProceedings of the International Joint Conference on Neural Networks
dc.description.page-
dc.description.coden85OFA
dc.identifier.isiutNOT_IN_WOS
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