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  <event:Event rdf:about="http://ebiquity.umbc.edu/event/html/id/260/Real-Time-Shape-From-Silhouette-using-Cell-B-E-">
    <rdfs:label><![CDATA[Real Time Shape From Silhouette using Cell B.E.]]></rdfs:label>
    <event:title><![CDATA[Real Time Shape From Silhouette using Cell B.E.]]></event:title>
    <event:speaker><person:MSStudent rdf:about="http://ebiquity.umbc.edu/person/html/David/Chapman/"><person:name><![CDATA[David  Chapman]]></person:name><rdfs:label><![CDATA[David  Chapman]]></rdfs:label></person:MSStudent></event:speaker>
    <event:startDate rdf:datatype="&xsd;dateTime">2008-09-30T10:30:00-05:00</event:startDate>
    <event:endDate rdf:datatype="&xsd;dateTime">2008-09-30T12:00:00-05:00</event:endDate>
    <event:location><![CDATA[ITE 325b]]></event:location>
    <event:abstract><![CDATA[The Shape From Silhouette (SFS) algorithm is a well known approach to 3D
shape detection from 2D camera images.  It is also a performance
bottleneck in markerless motion capture technology for use in move
production.  We have accelerated SFS using Cell B.E., a heterogeneous
multicore chip with 200 GFLOPS peek performance.  However peek performance
requires highly vectorizable algorithms. We parallelize SFS in a novel
way.  Our approach demonstrates bitwise SIMD parallelism, sequential RAM
access, and power of 2 data alignment.  Our implementation on Cell B.E.
demonstrates near real time performance at high voxel resolutions.
]]></event:abstract>
    <event:uri><![CDATA[http://ebiquity.umbc.edu/]]></event:uri>
    <event:tag><![CDATA[multi-core computing]]></event:tag>
    <event:tag><![CDATA[cell processor]]></event:tag>
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