JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Feature Point Filtering Method Based on CS-RANSAC for Efficient Planar Homography Estimating
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Feature Point Filtering Method Based on CS-RANSAC for Efficient Planar Homography Estimating
Kim, Dae-Woo; Yoon, Ui-Nyoung; Jo, Geun-Sik;
  PDF(new window)
 Abstract
Markerless tracking for augmented reality using Homography can augment virtual objects correctly and naturally on live view of real-world environment by using correct pose and direction of camera. The RANSAC algorithm is widely used for estimating Homography. CS-RANSAC algorithm is one of the novel algorithm which cooperates a constraint satisfaction problem(CSP) into RANSAC algorithm for increasing accuracy and decreasing processing time. However, CS-RANSAC algorithm can be degraded performance of calculating Homography that is caused by selecting feature points which estimate low accuracy Homography in the sampling step. In this paper, we propose feature point filtering method based on CS-RANSAC for efficient planar Homography estimating the proposed algorithm evaluate which feature points estimate high accuracy Homography for removing unnecessary feature point from the next sampling step using Symmetric Transfer Error to increase accuracy and decrease processing time. To evaluate our proposed method we have compared our algorithm with the bagic CS-RANSAC algorithm, and basic RANSAC algorithm in terms of processing time, error rate(Symmetric Transfer Error), and inlier rate. The experiment shows that the proposed method produces 5% decrease in processing time, 14% decrease in Symmetric Transfer Error, and higher accurate homography by comparing the basic CS-RANSAC algorithm.
 Keywords
RANSAC;CS-RANSAC;Homography;Symmetric Transfer Error;Feature Point;
 Language
Korean
 Cited by
 References
1.
R. T. Azuma, "A survey of augmented reality," Presence: Teleoperators and Virtual Environments, Vol.6, pp.355-385, 1997. crossref(new window)

2.
Z. Feng, H. B. -L. Duh, and M. Billinghurst, "Trends in augmented reality tracking, interaction and display: A review of ten years of ISMAR," Proceedings of International Symposium on Mixed and Augmented Reality, pp.193-202, 2008.

3.
P. Sturm, "Algorithms for plane-based pose estimation," International Conference on Computer Vision and Pattern Recognition, pp.706-711, 2000.

4.
S. J. D. Prince, K. Xu, and A. D. Cheok, "Augmented reality camera tracking with homographies," Computer Graphics, Vol.22, No.6, pp.39-45, 2002. crossref(new window)

5.
H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, "SURF: Speeded Up Robust Features," Computer Vision and Image Understanding, Vol.110, No.3, pp.346-359, 2008. crossref(new window)

6.
M. A. Fischler and R. C. Bolles, "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography," Communications of the ACM, Vol.24, pp.381-395, 1981. crossref(new window)

7.
RANSAC [Internet], http://en.wikipedia.org/wiki/RANSAC, 2015.

8.
O. Chum, J. Matas, and J. Kittler, "Locally Optimized RANSAC," in Proceedings of Deutsche Arbeitsgemeinschaft fur Mustererkennung, Germany, 2003.

9.
G. S. Jo, K. S. Lee, C. Devy, C. H. Jang, and M. H. Ga, "RANSAC versus CS-RANSAC," American Association for Artificial Intelligence(AAAI), pp.1350-1356, 2015.

10.
C. H. Jang, K. S. Lee, and G. S. Jo, "CSP Driven RANSAC Algorithm for Improving Accuracy of Planar Homography," Journal of Korean Institute of Information Science and Engineers, Vol.39, No.11, pp.876-888, 2012.

11.
R. Hartley and A. Zisserman, "Multiple View Geometry in computer vision," 2nd ed., Cambridge University Press, 2000.

12.
A. Criminisi, I. Reid, and A. Zisserman, "A Plane Measuring Device," Image and Vision Computing, Vol.17, pp.625-634, 1999. crossref(new window)

13.
Q. Wang, J. Mooser, S. You, and U. Neumann, "Augmented exhibitions using natural features," Int. J. Virtual Reality, Vol.7, No.4, pp.1-8, 2008.

14.
S. J. Russell and P. Norving, "Artificial Intelligence: A Modern Approach," 3rd ed., Pearson, 2009.

15.
UKbench dataset(Center for Visualization&Virtual Environments) [Internet], http://vis.uky.edu/-stewe/ukbench/.