Feature Matching Algorithm Robust To Viewpoint Change 


Vol. 40,  No. 12, pp. 2363-2371, Dec.  2015


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  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.

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  Cite this article

[IEEE Style]

H. Jung and J. Yoo, "Feature Matching Algorithm Robust To Viewpoint Change," The Journal of Korean Institute of Communications and Information Sciences, vol. 40, no. 12, pp. 2363-2371, 2015. DOI: .

[ACM Style]

Hyun-jo Jung and Ji-sang Yoo. 2015. Feature Matching Algorithm Robust To Viewpoint Change. The Journal of Korean Institute of Communications and Information Sciences, 40, 12, (2015), 2363-2371. DOI: .

[KICS Style]

Hyun-jo Jung and Ji-sang Yoo, "Feature Matching Algorithm Robust To Viewpoint Change," The Journal of Korean Institute of Communications and Information Sciences, vol. 40, no. 12, pp. 2363-2371, 12. 2015.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880