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Image Scale Prediction Using Key-point Clusters on Multi-scale Image Space  

Ryu, kwon-Yeal (Division of Software Engineerings, Uiduk University)
Publication Information
Journal of the Institute of Convergence Signal Processing / v.19, no.1, 2018 , pp. 1-6 More about this Journal
Abstract
In this paper, we propose the method to eliminate repetitive processes for key-point detection on multi-scale image space. The proposed method detects key-points from the original image, and select a good key-points using the cluster filters, and create the key-point clusters. And it select reference objects by using direction angles of the key-point clusters, predict the scale of the original image by using the distributed distance ratio. It transform the scale of the reference image, and apply the detection of key-points to the transformed reference image. In the results of the experiment, the proposed method can be found to improve the key-points detection time by 75 % and 71 % compared to SIFT method and scaled ORB method using the multi-scale images.
Keywords
Scale prediction; Image matching; Key point cluster; Multi-scale image; Distributed distance ratio;
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  • Reference
1 D. Lowe, "Distinctive Image Features from Scale Invariant Key-points," IJCV, 2004
2 H. Bay, "Speeded-Up Robust Features(SURF)," ECCV, 2008
3 Ethan Rublee, "ORB : an efficient alternative to SIFT or SURF," ICCV, 2011
4 Stefan Leutenegger, "BRISK : Binary Robust Invariant Scalable Key-points;" ICCV, 2011
5 Zhu, Ye, Xuanjing Shen, and Haipeng Chen. "Copy move forgery detection based on scaled ORB." Multimedia Tools and Applications, pp. 1-13. 2015