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dc.contributor.authorWestling, Fredrick Andrers
dc.contributor.authorSun, Changming
dc.contributor.authorWang, Dadong
dc.contributor.authorAlam, Fahim
dc.contributor.editorJ. Zhou, X. Bai and T. Caelli
dc.date.accessioned2019-01-09T03:47:17Z
dc.date.available2019-01-09T03:47:17Z
dc.date.issued2016
dc.identifier.isbn9781466694354
dc.identifier.doi10.4018/978-1-4666-9435-4.ch003
dc.identifier.urihttp://hdl.handle.net/10072/141822
dc.description.abstractAn approach is suggested for automating fish identification and measurement using stereo Baited Remote Underwater Video footage. Simple methods for identifying fish are not sufficient for measurement, since the snout and tail points must be found, and the stereo data should be incorporated to find a true measurement. We present a modular framework that ties together various approaches in order to develop a generalized system for automated fish detection and measurement. A method is also suggested for using machine learning to improve identification. Experimental results indicate the suitability of our approach.
dc.description.peerreviewedYes
dc.languageenglish
dc.language.isoeng
dc.publisherInformation Science Reference
dc.publisher.placeUnited States
dc.relation.ispartofbooktitleComputer Vision and Pattern Recognition in Environmental Informatics
dc.relation.ispartofchapter3
dc.relation.ispartofpagefrom41
dc.relation.ispartofpageto57
dc.subject.fieldofresearchPattern Recognition and Data Mining
dc.subject.fieldofresearchcode080109
dc.titleFish Counting and Measurement: A Modular Framework and Implementation
dc.typeBook chapter
dc.type.descriptionB2 - Chapters (Other)
dc.type.codeB - Book Chapters
gro.hasfulltextNo Full Text
gro.griffith.authorAlam, Fahim


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