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http://dx.doi.org/10.7840/kics.2015.40.12.2363

Feature Matching Algorithm Robust To Viewpoint Change  

Jung, Hyun-jo (Kwangwoon University)
Yoo, Ji-sang (Kwangwoon University)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.40, no.12, 2015 , pp. 2363-2371 More about this Journal
Abstract
In this paper, we propose a new feature matching algorithm which is robust to the viewpoint change by using the FAST(Features from Accelerated Segment Test) feature detector and the SIFT(Scale Invariant Feature Transform) feature descriptor. The original FAST algorithm unnecessarily results in many feature points along the edges in the image. To solve this problem, we apply the principal curvatures for refining it. We use the SIFT descriptor to describe the extracted feature points and calculate the homography matrix through the RANSAC(RANdom SAmple Consensus) with the matching pairs obtained from the two different viewpoint images. To make feature matching robust to the viewpoint change, we classify the matching pairs by calculating the Euclidean distance between the transformed coordinates by the homography transformation with feature points in the reference image and the coordinates of the feature points in the different viewpoint image. Through the experimental results, it is shown that the proposed algorithm has better performance than the conventional feature matching algorithms even though it has much less computational load.
Keywords
viewpoint change; homography transform; Mashing;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 D. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vision, vol. 60, no. 2, pp. 91-110, Nov. 2004.   DOI
2 K. Mikolajczyk, "Scale & affine invariant interest point detectors," Int. J. Comput. Vision, vol. 60, no. 1, pp. 63-86, Oct. 2004.   DOI
3 K. Mikolajczyk, "A performance evaluation of local descriptors," Pattern Anal. and Machine Intell., vol. 27, no. 10, pp. 1615-1630, Oct. 2005.   DOI
4 K. Midolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. kadir, and L. Van Gool. "A comparison of affine region detectors," Int. J. Comput. Vision, vol. 65, no. 1-2, pp. 43-72, Nov. 2005.   DOI
5 E. Rosten and T. Drummond, "Machine learning for high-speed corner detection," 9th Eur. Conf. Comput. Vision, Graz, Austria, pp. 430-443, May 2006.
6 E. Rosten, "Faster and better : A machine learning approach to corner detection," Pattern Anal. and Machine Intell., vol. 32, no. 1, pp. 105-119, Jan. 2010.   DOI
7 H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, "Speeded-up robust feature," Computer Vision and Image Understanding, vol. 10, no. 3, pp. 346-359, Jun. 2008.
8 M. A. Fischler and R. C. Bolles, "Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography," Commun. ACM, vol. 24, no. 6, pp. 381-395, Jun. 1981.   DOI
9 D. Comaniciu, V. Ramesh, and P. Meer, "Real-time tracking of non-rigid objects using mean shift," in Proc. 2000 IEEE Conf. Comput. Vision and Pattern Recognition, vol. 2, pp. 142-149, Jun. 2000.
10 http://www.robots.ox.ac.uk/-vgg/research/affine/
11 http://www.vision.caltech.edu/pmoreels/Datasets/
12 M. M. Hossain, H. J. Lee, and J. S. Lee, "Fast image stitching for video stabilization using sift feature points," J. KICS, vol. 39, no. 10, pp. 957-966, Oct. 2014.
13 B. W. Chung, K. Y. Park, and S. Y. Hwang, "A fast and efficient haar-like feature selection algorithm for object detection," J. KICS, vol. 38, no. 6, pp. 486-497, Jun. 2013.
14 H. K. Jang, "The more environmentally robust edge detection of moving objects using improved Canny edge detector and Freeman chain code," J. KICS, vol. 37, no. 2, pp. 37-42, Apr. 2012.