Opencv 中的单应矩阵？

Question

In LATCH_match.cpp in opencv_3.1.0 单应矩阵定义并用作：

Mat homography;
FileStorage fs("../data/H1to3p.xml", FileStorage::READ);
...
fs.getFirstTopLevelNode() >> homography;
...
Mat col = Mat::ones(3, 1, CV_64F);
col.at<double>(0) = matched1[i].pt.x;
col.at<double>(1) = matched1[i].pt.y;
col = homography * col;
...

为什么 H1to3p.xml 是：

<opencv_storage><H13 type_id="opencv-matrix"><rows>3</rows><cols>3</cols><dt>d</dt><data>
    7.6285898e-01  -2.9922929e-01   2.2567123e+02
    3.3443473e-01   1.0143901e+00  -7.6999973e+01
    3.4663091e-04  -1.4364524e-05   1.0000000e+00 </data></H13></opencv_storage>

这些数字是根据什么标准选择的？它们可以用于过滤关键点的任何其他单应性测试（如 LATCH_match.cpp）？

Answer 1

我假设你的“LATCH_match.cpp in opencv_3.1.0”是 https://github.com/Itseez/opencv/blob/3.1.0/samples/cpp/tutorial_code/xfeatures2D/LATCH_match.cpp

在该文件中，您 find:

// If you find this code useful, please add a reference to the following paper in your work:
// Gil Levi and Tal Hassner, "LATCH: Learned Arrangements of Three Patch Codes", arXiv preprint arXiv:1501.03719, 15 Jan. 2015

因此，查看 http://arxiv.org/pdf/1501.03719v1.pdf 你会发现

For each set, we compare the first image against each of the remaining five and check for correspondences. Performance is measured using the code from [16, 17]¹ , which computes recall and 1-precision using known ground truth homographies between the images.

我认为图像 ../data/graf1.png 是我在这里显示的 https://github.com/Itseez/opencv/blob/3.1.0/samples/data/graf1.png:

根据评论 by Catree the original dataset is at http://www.robots.ox.ac.uk/~vgg/research/affine/det_eval_files/graf.tar.gz 那里说

Homographies between image pairs included.

所以我认为存储在文件../data/H1to3p.xml中的单应性是图像1和图像3之间的单应性

Opencv 中的单应矩阵？

Homography matrix in Opencv?

opencv

image-processing

homography