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Proceedings Paper

The usage of color invariance in SURF
Author(s): Gang Meng; Zhiguo Jiang; Danpei Zhao
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Paper Abstract

SURF (Scale Invariant Feature Transform) is a robust local invariant feature descriptor. However, SURF is mainly designed for gray images. In order to make use of the information provided by color (mainly RGB channels), this paper presents a novel colored local invariant feature descriptor, CISURF (Color Invariance based SURF). The proposed approach builds the descriptors in a color invariant space, which stems from Kubelka-Munk model and provides more valuable information than the gray space. Compared with the conventional SURF and SIFT descriptors, the experimental results show that descriptors created by CISURF is more robust to the circumstance changes such as the illumination direction, illumination intensity, and the viewpoints, and are more suitable for the deep space background objects.

Paper Details

Date Published: 30 October 2009
PDF: 7 pages
Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 749508 (30 October 2009); doi: 10.1117/12.833509
Show Author Affiliations
Gang Meng, Beijing Univ. of Aeronautics and Astronautics (China)
Zhiguo Jiang, Beijing Univ. of Aeronautics and Astronautics (China)
Danpei Zhao, Beijing Univ. of Aeronautics and Astronautics (China)


Published in SPIE Proceedings Vol. 7495:
MIPPR 2009: Automatic Target Recognition and Image Analysis

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