Browse > Article
http://dx.doi.org/10.5392/JKCA.2010.10.7.010

Multiple Homographies Estimation using a Guided Sequential RANSAC  

Park, Yong-Hee (미래로 시스템)
Kwon, Oh-Seok (충남대학교 컴퓨터공학과)
Publication Information
The Journal of the Korea Contents Association / v.10, no.7, 2010 , pp. 10-22 More about this Journal
Abstract
This study proposes a new method of multiple homographies estimation between two images. With a large proportion of outliers, RANSAC is a general and very successful robust parameter estimator. However it is limited by the assumption that a single model acounts for all of the data inliers. Therefore, it has been suggested to sequentially apply RANSAC to estimate multiple 2D projective transformations. In this case, because outliers stay in the correspondence data set through the estimation process sequentially, it tends to progress slowly for all models. And, it is difficult to parallelize the sequential process due to the estimation order by the number of inliers for each model. We introduce a guided sequential RANSAC algorithm, using the local model instances that have been obtained from RANSAC procedure, which is able to reduce the number of random samples and deal simultaneously with multiple models.
Keywords
Homography; RANSAC; 2D Projective Transformation; Robust Parameter Estimation; Outlier;
Citations & Related Records
연도 인용수 순위
  • Reference
1 O. Chum and J. Matas, "Randomized RANSAC with sequential probability ratio test," Proceedings of the 10th International Conference on Computer Vision, pp.1727-1732, 2005.   DOI
2 David Nister, "Preemptive RANSAC for Live Structure and Motion Estimation," Proceedings of the 9th International Conference on Computer Vision, Vol.1, pp.199-206, 2003.   DOI
3 Etienne Vincent and Robert Laganiere, "Detecting Planar Homographies in an Image Pair," Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis, pp.182-187, 2001.   DOI
4 Yasushi Kanazawa and Hiroshi Kawakami, "Detection of Planar Regions with Uncalibrated Stereo using Distributions of Feature Points," Proceedings of the 15th British Machine Vision Conference, Vol.1, pp.247-256, 2004.
5 M. Zuliani, C. S. Kenney, and B. S. Manjunath, "The MultiRANSAC Algorithm and its Application to Detect Planar Homographies," Proceedings of the International Conference on Image Processing, Vol.3, pp.153-156, 2005.   DOI
6 Macro Zuliani, RANSAC toolbox for Matlab, MATLAB Central, 2008.
7 http://www.robots.ox.ac.uk/～vgg/
8 Tomas Werner and Andrew Zisserman, "New Techniques for Automated Architecture Reconstruction from Photographs," Proceedings of the 7th European Conference on Computer Vision, pp.541-555, 2002.
9 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, No.6, pp.381-395, 1981.   DOI   ScienceOn
10 Liran Goshen and Ilan Shimshoni, "Guided Sampling via Weak Motion Models and Outlier Sample Generation for Epipolar Geometry Estimation," International Journal of Computer Vision, Vol.80, No.2, pp.275-288, 2008.   DOI
11 Rahul Raguram, Jan-Michael Frahm, and Marc Pollefeys, "A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus," Proceedings of the 10th European Conference on Computer Vision, pp.500-513, 2008.
12 C. M. Cheng and S. H. Lai, "A consensus sampling technique for fast and robust model fitting," Pattern Recognition, Vol.42, No.7, pp.1318-1329, 2009.   DOI   ScienceOn
13 Yihong Wu, Youfu Li, and Zhanyi Hu, "Detecting and Handling Unreliable Points for Camera Parameter Estimation," International Journal of Computer Vision, Vol.79, No.2, pp.209-223, 2008.   DOI
14 P. H. S. Torr and A. Zisserman, "MLESAC: A new robust estimator with application to estimating image geometry," Computer Vision and Image Understanding, Vol.78, pp.138-156, 2000.   DOI   ScienceOn
15 Ben Tordoff and David W. Murray, "Guided sampling and consensus for motion estimation," Proceedings of the 7th European Conference on Computer Vision, pp.82-98, 2002.
16 O. Chum and J. Matas, "Matching with PROSAC – progressive sample consensus," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Vol.1, pp.220-226, 2005.
17 O. Chum and J. Matas, "Randomized RANSAC with Td,d test," Image and Vision Computing, Vol.22, No.10, pp.837-842, 2004.   DOI   ScienceOn
18 O. Chum, "An effective bail-out test for RANSAC consensus scoring," Proceedings of the 16th British Machine Vision Conference, pp.629-638, 2005.
19 Evan Ribnick, Stefan Atev, and Nokolaos P. Papanikolopoulos, "Estimating 3D Positions and Velocities of Projectiles from Monocular Views," IEEE Trans. on PAMI, Vol.31, No.5, pp.938-944, 2009.   DOI   ScienceOn
20 Alec Mills and Gregory Dudek, "Image stitching with dynamic elements," Image and Vision Computing, Vol.27, No.10, pp.1593-1602, 2009.   DOI   ScienceOn
21 http://www.2d3.com
22 J. B. Hayet, F. Lerasle, and M. Devy, "A visual landmark framework for mobile robot navigation," Image and Vision Computing, Vol.25, No.28, pp.1341-1351, 2007.   DOI   ScienceOn
23 Heung-Yeung Shum and Richard Szeliski, "Construction of panoramic image mosaics with global and local alignment," International Journal of Computer Vision, Vol.36, No.2, pp.101-130, 2000.   DOI
24 Cheng-Yuan Tang, "Self-Calibration for Metric 3D Reconstruction Using Homography," IAPR Conference on Machine Vision Applications, pp.86-89, 2007.
25 Zhengyou Zhang, "A Flexible New Technique for Camera Calibration," IEEE Trans. on PAMI, Vol.22, No.11, pp.1330-1334, 2000.   DOI   ScienceOn
26 Bill Triggs and Andrew Zisserman, "Feature Based Methods for Structure and Motion Estimation," Proceedings of the International Workshop on Vision Algorithms: Theory and Practice, pp.278-294, 1999.
27 Richard Hartley and Andrew Zisserman, Multiple View Geometry in Computer Vision, 2nd Edition, Cambridge University Press, 2003.
28 Zhengyou Zhang, Rachid Deriche, Olivier Faugeras, and Quang-Tuan Luong, "A Robust Technique for Matching Two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry," Artificial Intelligence, Vol.78, pp.87-119, 1995.   DOI   ScienceOn