Stereo matching of objects with same features based on delaunay triangulation and affine constraint

被引：0
作者：
Wang X. [1 ,2 ]
Xing F. [1 ,2 ]
Liu F. [1 ,2 ]
机构：
[1] State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin
[2] Key Laboratory of MOEMS of the Ministry of Education, Tianjin University, Tianjin
来源：
Liu, Feng (tjuliufeng@tju.edu.cn) | 1600年 / Chinese Optical Society卷 / 36期
关键词：
Affine constraint; Affine scale-invariant feature transform algorithm; Delaunay triangulation; Machine vision; Multiple objects with same features; Stereo matching;
D O I：
10.3788/AOS201636.1115004
中图分类号：
学科分类号：
摘要：
For practical demand of the localization of multiple random objects with large view filed, long distance and same features, a 3D coordinate measuring system is established based on the binocular stereo vision theory. To precisely position the multiple random objects with the same features, the multiple objects need matching correctly. An innovative method based on the Delaunay triangulation and affine constraint is proposed to achieve correct matching of the multiple objects with same features. The matching points on the background images are obtained with the affine scale-invariant feature transform (ASIFT) algorithm that has an anti-affine transformation. The Delaunay triangulation algorithm is used to generate triangular meshes by the seed points. The affine matrix of the triangular region is calculated by using vertexes of matched triangles. According to the distribution of object points in different matched triangles, the multiple objects with same features will be matched by the affine constraint. Experimental results show that the proposed method realizes the fast and efficient matching of multiple objects with same features. The time of object extraction and real-time matching is about 30 ms, which satisfies the requirement of 25 frame/s real-time processing for cameras. The proposed method solves the problem of matching of multiple objects with same features on the arc slope in large 3D space. © 2016, Chinese Lasers Press. All right reserved.
引用
收藏
页数：8
相关论文
共 16 条
  • [11] Wei S., Wang S., Zhou C., Et al., An iterative closest point algorithm based on biunique correspondence of point clouds for 3D reconstruction, Acta Optica Sinica, 35, 5, (2015)
  • [12] Morel J.M., Yu G., ASIFT: A new framework for fully affine invariant image comparison, SIAM Journal on Imaging Sciences, 2, 2, pp. 438-469, (2009)
  • [13] Fischler M.A., Bolles R.C., Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography, pp. 726-740, (1987)
  • [14] Xu X., Sun S., Sha Y., Et al., A method of infrared image mosaic based on improved RANSAC, Laser & Optoelectronics Progress, 51, 11, (2014)
  • [15] McCool M., Fiume E., Hierarchical poisson disk sampling distributions, Proceedings of the Conference on Graphics Interface, pp. 94-105, (1992)
  • [16] Zhao M., An B., Wu Y., Et al., A robust Delaunay triangulation matching for multispectral/multidate remote sensing image registration, IEEE Geoscience & Remote Sensing Letters, 12, 4, pp. 711-715, (2015)
12