IEICE TRANSACTIONS on Information
Fast Detection of Robust Features by Reducing the Number of Box Filtering in SURF
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.3 pp.725-728
- Publication Date
- 2011/03/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.725
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Hanhoon PARK, Hideki MITSUMINE, Mahito FUJII, "Fast Detection of Robust Features by Reducing the Number of Box Filtering in SURF" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 3, pp. 725-728, March 2011, doi: 10.1587/transinf.E94.D.725.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.725/_p
Copy
@ARTICLE{e94-d_3_725,
author={Hanhoon PARK, Hideki MITSUMINE, Mahito FUJII, },
journal={IEICE TRANSACTIONS on Information},
title={Fast Detection of Robust Features by Reducing the Number of Box Filtering in SURF},
year={2011},
volume={E94-D},
number={3},
pages={725-728},
abstract={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.},
keywords={},
doi={10.1587/transinf.E94.D.725},
ISSN={1745-1361},
month={March},}
Copy
TY - JOUR
TI - Fast Detection of Robust Features by Reducing the Number of Box Filtering in SURF
T2 - IEICE TRANSACTIONS on Information
SP - 725
EP - 728
AU - Hanhoon PARK
AU - Hideki MITSUMINE
AU - Mahito FUJII
PY - 2011
DO - 10.1587/transinf.E94.D.725
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2011
AB - 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.
ER -
English
