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<meta content="Fearn, Robert" name="eprints.creators_name" />
<meta content="Williams, Raymond" name="eprints.creators_name" />
<meta content="Cameron-Jones, Mike" name="eprints.creators_name" />
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<meta content="Modern fisheries management for scallop fisheries requires accurate assessment of fish stocks and careful monitoring of marine habitats. Underwater video promises to provide a means of obtaining this information in a cost effective and non-destructive way, but a major limitation is the need for time-consuming and subjective manual analysis of the video data obtained. This poster reports on research, conducted in conjunction with the Tasmanian Aquaculture and Fisheries Institute, focusses on the development of computer vision techniques to automate the process of counting scallops depicted in video image sequences. The techniques will locate objects of interest, analyse the objects to determine which are scallops and then identify the scallop species" name="eprints.abstract" />
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<meta content="Fearn, Robert and Williams, Raymond and Cameron-Jones, Mike (2006) Artificial Intelligence techniques for marine video analysis. In: Australian Society for Fish Biology 2006 Conference and Workshop on Cutting Edge Technologies in Fish and Fisheries Science, 28 Aug - 1 Sept 2006, Hobart." name="eprints.citation" />
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<meta content="Modern fisheries management for scallop fisheries requires accurate assessment of fish stocks and careful monitoring of marine habitats. Underwater video promises to provide a means of obtaining this information in a cost effective and non-destructive way, but a major limitation is the need for time-consuming and subjective manual analysis of the video data obtained. This poster reports on research, conducted in conjunction with the Tasmanian Aquaculture and Fisheries Institute, focusses on the development of computer vision techniques to automate the process of counting scallops depicted in video image sequences. The techniques will locate objects of interest, analyse the objects to determine which are scallops and then identify the scallop species" name="DC.description" />
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<h1 class="ep_tm_pagetitle">Artificial Intelligence techniques for marine video analysis</h1>
<p style="margin-bottom: 1em" class="not_ep_block"><span class="person_name">Fearn, Robert</span> and <span class="person_name">Williams, Raymond</span> and <span class="person_name">Cameron-Jones, Mike</span> (2006) <xhtml:em>Artificial Intelligence techniques for marine video analysis.</xhtml:em> In: Australian Society for Fish Biology 2006 Conference and Workshop on Cutting Edge Technologies in Fish and Fisheries Science, 28 Aug - 1 Sept 2006, Hobart.</p><p style="margin-bottom: 1em" class="not_ep_block"></p><table style="margin-bottom: 1em" class="not_ep_block"><tr><td valign="top" style="text-align:center"><a onmouseover="EPJS_ShowPreview( event, 'doc_preview_697' );" href="http://eprints.utas.edu.au/686/1/FishBiologyPoster.pdf" onmouseout="EPJS_HidePreview( event, 'doc_preview_697' );"><img alt="[img]" src="http://eprints.utas.edu.au/style/images/fileicons/application_pdf.png" class="ep_doc_icon" border="0" /></a><div class="ep_preview" id="doc_preview_697"><table><tr><td><img alt="" src="http://eprints.utas.edu.au/686/thumbnails/1/preview.png" class="ep_preview_image" border="0" /><div class="ep_preview_title">Preview</div></td></tr></table></div></td><td valign="top"><a href="http://eprints.utas.edu.au/686/1/FishBiologyPoster.pdf"><span class="ep_document_citation">PDF</span></a> - Requires a PDF viewer<br />184Kb</td></tr></table><div class="not_ep_block"><h2>Abstract</h2><p style="padding-bottom: 16px; text-align: left; margin: 1em auto 0em auto">Modern fisheries management for scallop fisheries requires accurate assessment of fish stocks and careful monitoring of marine habitats. Underwater video promises to provide a means of obtaining this information in a cost effective and non-destructive way, but a major limitation is the need for time-consuming and subjective manual analysis of the video data obtained. This poster reports on research, conducted in conjunction with the Tasmanian Aquaculture and Fisheries Institute, focusses on the development of computer vision techniques to automate the process of counting scallops depicted in video image sequences. The techniques will locate objects of interest, analyse the objects to determine which are scallops and then identify the scallop species</p></div><table style="margin-bottom: 1em" cellpadding="3" class="not_ep_block" border="0"><tr><th valign="top" class="ep_row">Item Type:</th><td valign="top" class="ep_row">Conference or Workshop Item (Poster)</td></tr><tr><th valign="top" class="ep_row">Keywords:</th><td valign="top" class="ep_row">artificial intelligence, automated video analysis, scallops stocks assessment, fisheries science, computer vision</td></tr><tr><th valign="top" class="ep_row">Subjects:</th><td valign="top" class="ep_row"><a href="http://eprints.utas.edu.au/view/subjects/280208.html">280000 Information, Computing and Communication Sciences > 280200 Artificial Intelligence and Signal and Image Processing > 280208 Computer Vision</a></td></tr><tr><th valign="top" class="ep_row">ID Code:</th><td valign="top" class="ep_row">686</td></tr><tr><th valign="top" class="ep_row">Deposited By:</th><td valign="top" class="ep_row"><span class="ep_name_citation"><span class="person_name">Dr Raymond Williams</span></span></td></tr><tr><th valign="top" class="ep_row">Deposited On:</th><td valign="top" class="ep_row">01 Feb 2007</td></tr><tr><th valign="top" class="ep_row">Last Modified:</th><td valign="top" class="ep_row">09 Jan 2008 02:30</td></tr><tr><th valign="top" class="ep_row">ePrint Statistics:</th><td valign="top" class="ep_row"><a target="ePrintStats" href="/es/index.php?action=show_detail_eprint;id=686;">View statistics for this ePrint</a></td></tr></table><p align="right">Repository Staff Only: <a href="http://eprints.utas.edu.au/cgi/users/home?screen=EPrint::View&eprintid=686">item control page</a></p>
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