Abstract
Traditional image descriptors tend to utilize integral derivative characterizing local features, like orientation histogram. However, integral-based derivative has a disadvantage in describing image texture details in smooth area. In this paper, we propose a novel framework for reestablishing orientation histogram based on adaptive fractional derivative, which is better at representing local feature. Then a general weighting scheme for orientation histogram is developed, which improves the accuracy of keypoints description. Finally, we demonstrate the utility of our formulation in implementing solutions for various keypoints descriptions tasks. To exercise our framework we have created a new SIFT and SURF application over images.
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Acknowledgement
This research is supported by the National Natural Science Foundation of China under Grant No. 61472267 and No. 61203048, Nature Foundation of Jiangsu Province under Grant No. BK2012166, a grant from the City University of Hong Kong (Project No. 7004220), the Open Foundation of Modern Enterprise Information Application Supporting Software Engineering Technology R&D Center of Jiangsu Province under Grant No. SK201206, and the Innovation Project of Graduate Student Training under Grant No. CXZZ13_0854.
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Si, S., Hu, F., Wang, Z., Bi, Z., Cheng, C., Li, Z. (2015). An Adaptive Approach for Keypoints Description Using Fractional Derivative. In: He, X., et al. Intelligence Science and Big Data Engineering. Image and Video Data Engineering. IScIDE 2015. Lecture Notes in Computer Science(), vol 9242. Springer, Cham. https://doi.org/10.1007/978-3-319-23989-7_62
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DOI: https://doi.org/10.1007/978-3-319-23989-7_62
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