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Description
  • This paper presents the improved version of a rapid object detector. This detector uses Waldboost classifier with Local Rank Differences features for face detection. Because of it's computational complexity, original detector was parallelized to take advantage of modern multicore processors. Further speedup was achieved by fixed step scaling with heavy use of SSE instruction set and template metaprogramming.
  • This paper presents the improved version of a rapid object detector. This detector uses Waldboost classifier with Local Rank Differences features for face detection. Because of it's computational complexity, original detector was parallelized to take advantage of modern multicore processors. Further speedup was achieved by fixed step scaling with heavy use of SSE instruction set and template metaprogramming. (en)
Title
  • Accelerated Object Detection
  • Accelerated Object Detection (en)
skos:prefLabel
  • Accelerated Object Detection
  • Accelerated Object Detection (en)
skos:notation
  • RIV/00216305:26230/09:PU82601!RIV10-MSM-26230___
http://linked.open...avai/riv/aktivita
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  • P(LC06008)
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  • 301739
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  • RIV/00216305:26230/09:PU82601
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  • object detection, classification, adaboost, waldboost, local rank differences, parallelization, OpenMP, SSE, template metaprogramming<br> (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [87A714085E1D]
http://linked.open...v/mistoKonaniAkce
  • FEKT VUT v Brně
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  • Brno
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  • Proceedings of the 15th Conference Student EEICT 2009
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  • Havel, Jiří
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number of pages
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  • Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií
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  • 978-80-214-3870-5
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  • 26230
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