JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Feature Matching Algorithm Robust To Viewpoint Change
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Feature Matching Algorithm Robust To Viewpoint Change
Jung, Hyun-jo; Yoo, Ji-sang;
  PDF(new window)
 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;
 Language
Korean
 Cited by
 References
1.
D. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vision, vol. 60, no. 2, pp. 91-110, Nov. 2004. crossref(new window)

2.
K. Mikolajczyk, "Scale & affine invariant interest point detectors," Int. J. Comput. Vision, vol. 60, no. 1, pp. 63-86, Oct. 2004. crossref(new window)

3.
K. Mikolajczyk, "A performance evaluation of local descriptors," Pattern Anal. and Machine Intell., vol. 27, no. 10, pp. 1615-1630, Oct. 2005. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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.