Browse > Article
http://dx.doi.org/10.3837/tiis.2020.09.012

A reliable quasi-dense corresponding points for structure from motion  

Oh, Jangseok (Agriculture Robotics & Automation Research Center, Korea Institute of Robotics and Technology Convergence)
Hong, Hyunggil (Agriculture Robotics & Automation Research Center, Korea Institute of Robotics and Technology Convergence)
Cho, Yongjun (Agriculture Robotics & Automation Research Center, Korea Institute of Robotics and Technology Convergence)
Yun, Haeyong (Agriculture Robotics & Automation Research Center, Korea Institute of Robotics and Technology Convergence)
Seo, Kap-Ho (Agriculture Robotics & Automation Research Center, Korea Institute of Robotics and Technology Convergence)
Kim, Hochul (Department of Radiological Science, Eulji University)
Kim, Mingi (Department of Electronic and Information Engineering, Korea University)
Lee, Onseok (Department of Medical IT Engineering, College of Medical Sciences, Soonchunhyang University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.14, no.9, 2020 , pp. 3782-3796 More about this Journal
Abstract
A three-dimensional (3D) reconstruction is an important research area in computer vision. The ability to detect and match features across multiple views of a scene is a critical initial step. The tracking matrix W obtained from a 3D reconstruction can be applied to structure from motion (SFM) algorithms for 3D modeling. We often fail to generate an acceptable number of features when processing face or medical images because such images typically contain large homogeneous regions with minimal variation in intensity. In this study, we seek to locate sufficient matching points not only in general images but also in face and medical images, where it is difficult to determine the feature points. The algorithm is implemented on an adaptive threshold value, a scale invariant feature transform (SIFT), affine SIFT, speeded up robust features (SURF), and affine SURF. By applying the algorithm to face and general images and studying the geometric errors, we can achieve quasi-dense matching points that satisfy well-functioning geometric constraints. We also demonstrate a 3D reconstruction with a respectable performance by applying a column space fitting algorithm, which is an SFM algorithm.
Keywords
scale-invariant feature transform; speeded up robust features; structure from motion; column space fitting; affine-model;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
1 B. Shin and J. Seo, "Experimental Optimal Choice Of Initial Candidate Inliers Of The Feature Pairs With Well-Ordering Property For The Sample Consensus Method In The Stitching Of Drone-based Aerial Images," KSII Transactions on Internet and Information Systems, vol. 14, no. 4, pp. 1648-1672, 2020.   DOI
2 Y. Rao, X. Ding and B. Fan, "An Efficent Method of Binocular Data Reconstruction," KSII Transactions on Internet and Information Systems, vol. 9, no. 9, pp. 3721-3737, 2015.   DOI
3 D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International journal of computer vision, vol. 60, no. 2, pp. 91-110, 2004.   DOI
4 H. Bay, A. Ess, T. Tuytelaars and L. Van Gool, "Speeded-up robust features (SURF)," Computer vision and image understanding, vol. 110, no. 3, pp. 346-359, 2008.   DOI
5 J. Zhu, W. Sun, B. Guo and C. Li, "Surf points based Moving Target Detection and Long-term Tracking in Aerial Videos," KSII Transactions on Internet and Information Systems, vol. 10, no. 11, pp. 5624-5638, 2016.   DOI
6 J. Oh, H. Kim, J. Koo, J. Yu, T. Kang, J. Lee, and M. Kim, "ROBPCA-SIFT: a feature point extraction method for the consistent with epipolar geometry in endoscopic images," Image and Vision Computing New Zealand New Zealand, 2006.
7 Z. Zhang and W. S. Lee, "Deep Graphical Feature Learning for the Feature Matching Problem," in Proc. of International Conference on Computer Vision (ICCV), pp. 5086-5095, 2019.
8 N. Ufer and B. Ommer, "Deep Semantic Feature Matching," in Proc. of International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5929-5938, 2017.
9 K. Choi, J. Oh, S. Choi, and M. Kim, "A robust Human Face Recognition algorithm for flexible situations using SIFT," in Proc. of International Forum on Medical Imaging in Asia, 2009.
10 J. Oh, H. Kim, S. Choi, K. Choi, S. Ha, O. Lee, and M. Kim, "A robust method of feature extraction from noised endoscopic images," in Proc. of International Forum on Medical Imaging in Asia, 2009.
11 P. F. Gotardo, and A. M. Martinez, "Computing smooth time trajectories for camera and deformable shape in structure from motion with occlusion," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 10, pp. 2051-2065, 2011.   DOI
12 E. Mouragnon, M. Lhuillier, M. Dhome, F. Dekeyser, and P. Sayd, "Generic and real-time structure from motion using local bundle adjustment," Image and Vision Computing, vol. 27, no. 8, pp. 1178-1193, 2009.   DOI
13 Y. Pang, W. Li, Y. Yuan, and J. Pan, "Fully affine invariant SURF for image matching," Neurocomputing, vol. 85, pp. 6-10, 2012.   DOI
14 A. Irschara, C. Zach, M. Klopschitz, and H. Bischof, "Large-scale, dense city reconstruction from user-contributed photos," Computer Vision and Image Understanding, vol. 116, no. 1, pp. 2-15, 2012.   DOI
15 M. Lhuillier, and S. Yu, "Manifold surface reconstruction of an environment from sparse Structure-from-Motion data," Computer Vision and Image Understanding, vol. 117, no. 11, pp. 1628-1644, 2013.   DOI
16 Y. Furukawa, and J. Ponce, "Accurate, dense, and robust multiview stereopsis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 8, pp. 1362-1376, 2010.   DOI
17 M. I. Lourakis, and A. A. Argyros, "SBA: A software package for generic sparse bundle adjustment," ACM Transactions on Mathematical Software (TOMS), vol. 36, no. 1, pp. 2, 2009.
18 J.-M. Morel, and G. Yu, "ASIFT: A new framework for fully affine invariant image comparison," SIAM Journal on Imaging Sciences, vol. 2, no. 2, pp. 438-469, 2009.   DOI
19 R. Hartley, and A. Zisserman, Multiple view geometry in computer vision, Cambridge university press, 2004.
20 C. Wu, "VisualSFM: A visual structure from motion system," http://ccwu.me/vsfm/, 2013.