Computing Matched-Epipolar Projections
Abstract
A new method is given for image rectification, the process of resampling pairs of stereo images taken from widely differing viewpoints in order to produce a pair of matched epipolar projections. These are projections in which the epipolar lines run parallel with the x-axis and disparities between the images are in the x-direction only. The method is based on an examination of the essential matrix of Longuet-Higgins (1981), which describes the epipolar geometry of the image pair. The approach taken is consistent with that advocated by O. Faugeras (1992) of avoiding camera calibration. A matrix called the epipolar transformation matrix is defined. It is used to determine a pair of 2-D projective transforms to be applied to the two images in order to match the epipolar lines. The advantages include the simplicity of the 2-D projective transformation, which allows very fast resampling, as well as subsequent simplification in identifying matched points and in scene reconstruction.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Cite
Text
Hartley and Gupta. "Computing Matched-Epipolar Projections." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1993. doi:10.1109/CVPR.1993.341076Markdown
[Hartley and Gupta. "Computing Matched-Epipolar Projections." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1993.](https://mlanthology.org/cvpr/1993/hartley1993cvpr-computing/) doi:10.1109/CVPR.1993.341076BibTeX
@inproceedings{hartley1993cvpr-computing,
title = {{Computing Matched-Epipolar Projections}},
author = {Hartley, Richard I. and Gupta, Rajiv},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {1993},
pages = {549-555},
doi = {10.1109/CVPR.1993.341076},
url = {https://mlanthology.org/cvpr/1993/hartley1993cvpr-computing/}
}