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dc.contributor.authorSharma, Ojaswa
dc.contributor.authorAnton, François
dc.contributor.editorChen, Min
dc.contributor.editorSkala, Václav
dc.date.accessioned2014-03-26T12:45:36Z
dc.date.available2014-03-26T12:45:36Z
dc.date.issued2009
dc.identifier.citationWSCG '2009: Full Papers Proceedings: The 17th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision in co-operation with EUROGRAPHICS: University of West Bohemia Plzen, Czech Republic, February 2 - 5, 2009, p. 153-160.en
dc.identifier.isbn978-80-86943-93-0
dc.identifier.urihttp://wscg.zcu.cz/WSCG2009/Papers_2009/!_WSCG2009_Full_final.zip
dc.identifier.urihttp://hdl.handle.net/11025/10901
dc.description.abstractAcoustic images present views of underwater dynamics, even in high depths. With multi-beam echo sounders (SONARs), it is possible to capture series of 2D high resolution acoustic images. 3D reconstruction of the water column and subsequent estimation of fish abundance and fish species identification is highly desirable for planning sustainable fisheries. Main hurdles in analysing acoustic images are the presence of speckle noise and the vast amount of acoustic data. This paper presents a level set formulation for simultaneous fish reconstruction and noise suppression from raw acoustic images. Despite the presence of speckle noise blobs, actual fish intensity values can be distinguished by extremely high values, varying exponentially from the background. Edge detection generally gives excessive false edges that are not reliable. Our approach to reconstruction is based on level set evolution using Mumford-Shah segmentation functional that does not depend on edges in an image. We use the implicit function in conjunction with the image to robustly estimate a threshold for suppressing noise in the image by solving a second differential equation. We provide details of our estimation of suppressing threshold and show its convergence as the evolution proceeds. We also present a GPU based streaming computation of the method using NVIDIA’s CUDA framework to handle large volume data-sets. Our implementation is optimised for memory usage to handle large volumes.en
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesWSCG '2009: Full Papers Proceedingsen
dc.rights© Václav Skala - UNION Agencyen
dc.subject3D rekonstrukcecs
dc.subjectlevel set methodcs
dc.subjectakustické obrazycs
dc.subjectpotlačení šumucs
dc.subjectgrafické procesorycs
dc.titleCUDA based Level Set Method for 3D Reconstruction of Fishes from Large Acoustic Dataen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.subject.translated3D reconstructionen
dc.subject.translatedlevel set methoden
dc.subject.translatedacoustic imagesen
dc.subject.translatednoise suppressionen
dc.subject.translatedgraphic processing unitsen
dc.type.statusPeer-revieweden
dc.type.driverinfo:eu-repo/semantics/conferenceObjecten
dc.type.driverinfo:eu-repo/semantics/publishedVersionen
Appears in Collections:WSCG '2009: Full Papers Proceedings

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