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http://dx.doi.org/10.3837/tiis.2013.07.010

MEGH: A New Affine Invariant Descriptor  

Dong, Xiaojie (Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University)
Liu, Erqi (China Aerospace Science & Industry Corp.)
Yang, Jie (Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University)
Wu, Qiang (University of Technology Sydney)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.7, no.7, 2013 , pp. 1690-1704 More about this Journal
Abstract
An affine invariant descriptor is proposed, which is able to well represent the affine covariant regions. Estimating main orientation is still problematic in many existing method, such as SIFT (scale invariant feature transform) and SURF (speeded up robust features). Instead of aligning the estimated main orientation, in this paper ellipse orientation is directly used. According to ellipse orientation, affine covariant regions are firstly divided into 4 sub-regions with equal angles. Since affine covariant regions are divided from the ellipse orientation, the divided sub-regions are rotation invariant regardless the rotation, if any, of ellipse. Meanwhile, the affine covariant regions are normalized into a circular region. In the end, the gradients of pixels in the circular region are calculated and the partition-based descriptor is created by using the gradients. Compared with the existing descriptors including MROGH, SIFT, GLOH, PCA-SIFT and spin images, the proposed descriptor demonstrates superior performance according to extensive experiments.
Keywords
Local Descriptor; SIFT; Image Registration; Performance Evaluation;
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Times Cited By KSCI : 1  (Citation Analysis)
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