Fully affine invariant SURF for image matching

Authors:
Yanwei Pang;Wei Li;Yuan Yuan;Jing Pan
Affiliations:
School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China;School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China;Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, ...;School of Electronic Engineering, Tianjin University of Education and Technology, Tianjin 300222, China
Venue:
Neurocomputing
Year:
2012

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Abstract

Fast and robust feature extraction is crucial for many computer vision applications such as image matching. The representative and the state-of-the-art image features include Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and Affine SIFT (ASIFT). However, neither of them is fully affine invariant and computation efficient at the same time. To overcome this problem, we propose in this paper a fully affine invariant SURF algorithm. The proposed algorithm makes full use of the affine invariant advantage of ASIFT and the efficient merit of SURF while avoids their drawbacks. Experimental results on applications of image matching demonstrate the robustness and efficiency of the proposed algorithm.