Browsing by Author "Messom, C.H."

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    Performance characteristics of a cost-effective medium-sized Beowulf cluster supercomputer
    (2003) Barczak, A.L.C.; Messom, C.H.; Johnson, M.J.
    This paper presents some performance results obtained from a new Beowulf cluster, the Helix, built at Massey University, Auckland funded by the Allan Wilson Center for Evolutionary Ecology. Issues concerning network latency and the effect of the switching fabric and network topology on performance are discussed. In order to assess how the system performed using the message passing interface (MPI), two test suites (mpptest and jumpshot) were used to provide a comprehensive network performance analysis. The performance of an older fast-ethernet/single processor based cluster is compared to the new Gigabit/SMP cluster. The Linpack performance of Helix is investigated. The Linpack Rmax rating of 234.8 Gflops puts the cluster at third place in the Australia/ New Zealand sublist of the Top500 supercomputers, an extremely good performance considering the commodity parts and its low cost (US$125000).
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    Real-time computation of Haar-like features at generic angles for detection algorithms
    (Massey University, 2006) Barczak, A.L.C.; Johnson, M.J.; Messom, C.H.
    This paper proposes a new approach to detect rotated objects at distinct angles using the Viola-Jones detector. The use of additional Integral Images makes an approximation the Haar-like features for any given angle. The proposed approach uses di erent types of Haar-like features, including features that compute areas at 45o, 26.5o and 63.5o of rotation. Given a trained classi er (using normal features) a conversion is made using a pair of features so an equivalent value is computed for any angle. This conversion is only an approximation, but the errors are constrained and they would have limited impact on the nal accuracy of the classi er. We discuss the sources of errors in the computation of the Haar-like features and show through experiments that in natural images the errors are often negligible.