Human Detection Using Surf and Sift Feature Extraction Methods in Different Color Spaces
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Authors
Osameh Biglari
- Taali university, Qom, Iran.
Reza Ahsan
- Islamic Azad University, Qom branch, Iran.
Majid Rahi
- Pardisan University, Mazandaran, Feridonkenar, Iran.
Abstract
Local feature matching has become a commonly used method to compare images. For tracking
and human detection, a reliable method for comparing images can constitute a key component for
localization and loop closing tasks. two different types of image feature algorithms, Scale -
Invariant Feature Transform (SIFT) and the more recent Speeded Up Robust Features (SURF),
have been used to compare the images. In this paper, we propose the use of a rich set of feature
descriptors based on SIFT and SURF in the different color spaces.
Share and Cite
ISRP Style
Osameh Biglari, Reza Ahsan, Majid Rahi, Human Detection Using Surf and Sift Feature Extraction Methods in Different Color Spaces , Journal of Mathematics and Computer Science, 11 (2014), no. 2, 111 - 122
AMA Style
Biglari Osameh, Ahsan Reza, Rahi Majid, Human Detection Using Surf and Sift Feature Extraction Methods in Different Color Spaces . J Math Comput SCI-JM. (2014); 11(2):111 - 122
Chicago/Turabian Style
Biglari, Osameh, Ahsan, Reza, Rahi, Majid. "Human Detection Using Surf and Sift Feature Extraction Methods in Different Color Spaces ." Journal of Mathematics and Computer Science, 11, no. 2 (2014): 111 - 122
Keywords
- human detection
- SIFT
- SURF
- Color Spaces
- grayscale
MSC
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