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

Hanhoon PARK  Hideki MITSUMINE  Mahito FUJII  

IEICE TRANSACTIONS on Information and Systems   Vol.E94-D   No.3   pp.725-728
Publication Date: 2011/03/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E94.D.725
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
ordinal convolution,  fast feature detection,  SURF,  

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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.