Browse > Article
http://dx.doi.org/10.3745/KTSDE.2017.6.6.315

Improved CS-RANSAC Algorithm Using K-Means Clustering  

Ko, Seunghyun (인하대학교 컴퓨터공학과)
Yoon, Ui-Nyoung (인하대학교 컴퓨터공학과)
Alikhanov, Jumabek (인하대학교 컴퓨터정보공학과)
Jo, Geun-Sik (인하대학교 컴퓨터정보공학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.6, no.6, 2017 , pp. 315-320 More about this Journal
Abstract
Estimating the correct pose of augmented objects on the real camera view efficiently is one of the most important questions in image tracking area. In computer vision, Homography is used for camera pose estimation in augmented reality system with markerless. To estimating Homography, several algorithm like SURF features which extracted from images are used. Based on extracted features, Homography is estimated. For this purpose, RANSAC algorithm is well used to estimate homography and DCS-RANSAC algorithm is researched which apply constraints dynamically based on Constraint Satisfaction Problem to improve performance. In DCS-RANSAC, however, the dataset is based on pattern of feature distribution of images manually, so this algorithm cannot classify the input image, pattern of feature distribution is not recognized in DCS-RANSAC algorithm, which lead to reduce it's performance. To improve this problem, we suggest the KCS-RANSAC algorithm using K-means clustering in CS-RANSAC to cluster the images automatically based on pattern of feature distribution and apply constraints to each image groups. The suggested algorithm cluster the images automatically and apply the constraints to each clustered image groups. The experiment result shows that our KCS-RANSAC algorithm outperformed the DCS-RANSAC algorithm in terms of speed, accuracy, and inlier rate.
Keywords
RANSAC; CS_RANSAC; K-Means Clustering;
Citations & Related Records
연도 인용수 순위
  • Reference
1 R. T. Azuma, "A survey of augmented reality," Presence: Teleoperators and Virtual Environments, Vol.6, Issue 4, pp.355-385, 1997.   DOI
2 F. Zhou, H. B. Duh, and M. Billinghurst, "Trends Augmented Reality Tracking, Interaction and Display: A Review of Ten Years of ISMAR," IEEE International Symposium on Mixed and Augmented Reality, pp.193-202, 2008.
3 F. D. Crescenzio, M. Fantini, F. Persiani, L. D. Stefano, P. Azzari, and S. Salti, "Augmented Reality for Aircraft Maintenance Training and Operations Support," IEEE Computer Graphics and Applications, Vol.31, Issue 1, pp.96-101, 2011.   DOI
4 K. S. Lee, A. N. Rosli and G. S. Jo, "A Method for Automatically Creating an Interactive Semantic Video based on AR System," IEEE International Conference on Systems, Man, and Cybernetics, in press, 2012.
5 H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, L. V., "SURF: Speeded Up Robust Features," Computer Vision and Image Understanding, Vol.110, No.3, pp.346-359, 2008.   DOI
6 O. Faugeras and F. Lustman, "Motion and structure from motion in a piecewise planar environment," International Journal of Pattern Recognition and Artificial Intelligence, Vol.2, No.3, pp.485-508, 1988.   DOI
7 D. Stricker, "Tracking with Reference Images: A Real-Time and Markerless Tracking Solution for Out-Door Augmented Reality Applications," International Symposium on Virtual Reality, Archeology, and Cultural Heritage, ACM, pp.77-82, 2001.
8 E. Vincent and R. Laganiere, "Detecting planar homographies in an image pair," IEEE International Symposium on Image and Signal Processing and Analysis, pp.182-187, 2001.
9 RANSAC [Internet], http://en.wikipedia.org/wiki/RANSAC, 2016.
10 UKbench dataset (Center for Visualization&Virtual Environ ments) 2016 [Internet], http://vis.uky.edu/-stewe/ukbench/.
11 B. Subbiah and S. Christopher, "Image Classification through Integrated K-means Algorithm," International Journal of Computer Science Issues, Vol.9, No.2, pp.518-524, 2012.
12 D. Chandra, "Automatic Determination of Constraint Parameter for Improving Homography Matrix Calculation in RANSAC Algorithm," Korea Information Processing Society, Vol.21, No.1, 2014.
13 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.