Fast Detection of Robust Features by Reducing the Number of Box Filtering in SURF

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概要

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• Speeded up robust features (SURF) can detect scale- and rotation-invariant features at high speed by relying on integral images for image convolutions. However, since the number of image convolutions greatly increases in proportion to the image size, another method for reducing the time for detecting features is required. In this letter, we propose a method, called ordinal convolution, of reducing the number of image convolutions for fast feature detection in SURF and compare it with a previous method based on sparse sampling.

• 2011-03-01

著者

• Fujii Mahito

  Nhk (japan Broadcasting Corporation) Science & Technology Research Laboratories

• Mitsumine Hideki

  Nhk (japan Broadcasting Corporation) Science & Technology Research Laboratories

• PARK Hanhoon

  NHK (Japan Broadcasting Corporation) Science & Technology Research Laboratories

• Park Hanhoon

  Nhk (japan Broadcasting Corporation) Science & Technology Research Laboratories

関連論文

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