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Improving scale invariant feature transform-based descriptors with shape–color alliance robust feature

[+] Author Affiliations
Rui Wang, Zhengdan Zhu

Beihang University, School of Instrumentation Science and Opto-Electronics Engineering, Laboratory of Precision Opto-Mechatronics Technology, No. 37 Xueyuan Road, Haidian District, Beijing 100191, China

Liang Zhang

University of Connecticut, Department of Electrical and Computer Engineering, 371 Fairfield Way, U-2157, Storrs, Connecticut 06269, United States

J. Electron. Imaging. 24(3), 033002 (May 07, 2015). doi:10.1117/1.JEI.24.3.033002
History: Received July 7, 2014; Accepted April 8, 2015
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Abstract.  Constructing appropriate descriptors for interest points in image matching is a critical aspect task in computer vision and pattern recognition. A method as an extension of the scale invariant feature transform (SIFT) descriptor called shape–color alliance robust feature (SCARF) descriptor is presented. To address the problem that SIFT is designed mainly for gray images and lack of global information for feature points, the proposed approach improves the SIFT descriptor by means of a concentric-rings model, as well as integrating the color invariant space and shape context with SIFT to construct the SCARF descriptor. The SCARF method developed is more robust than the conventional SIFT with respect to not only the color and photometrical variations but also the measuring similarity as a global variation between two shapes. A comparative evaluation of different descriptors is carried out showing that the SCARF approach provides better results than the other four state-of-the-art related methods.

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Citation

Rui Wang ; Zhengdan Zhu and Liang Zhang
"Improving scale invariant feature transform-based descriptors with shape–color alliance robust feature", J. Electron. Imaging. 24(3), 033002 (May 07, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.3.033002


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