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fix latex script in the docs

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hyrodium 2021-05-26 00:21:10 +09:00
parent 4a2adba8f4
commit 81567a9d3e
3 changed files with 7 additions and 7 deletions
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8
doc/py_tutorials/py_feature2d/py_features_harris/py_features_harris.markdown
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@ -40,12 +40,12 @@ using **cv.Sobel()**).
Then comes the main part. After this, they created a score, basically an equation, which
determines if a window can contain a corner or not.
\f[R = det(M) - k(trace(M))^2\f]
\f[R = \det(M) - k(\operatorname{trace}(M))^2\f]
where
- \f$det(M) = \lambda_1 \lambda_2\f$
- \f$trace(M) = \lambda_1 + \lambda_2\f$
- \f$\lambda_1\f$ and \f$\lambda_2\f$ are the eigenvalues of M
- \f$\det(M) = \lambda_1 \lambda_2\f$
- \f$\operatorname{trace}(M) = \lambda_1 + \lambda_2\f$
- \f$\lambda_1\f$ and \f$\lambda_2\f$ are the eigenvalues of \f$M\f$
So the magnitudes of these eigenvalues decide whether a region is a corner, an edge, or flat.

4
doc/py_tutorials/py_feature2d/py_shi_tomasi/py_shi_tomasi.markdown
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@ -20,7 +20,7 @@ Harris Corner Detector. The scoring function in Harris Corner Detector was given
Instead of this, Shi-Tomasi proposed:
\f[R = min(\lambda_1, \lambda_2)\f]
\f[R = \min(\lambda_1, \lambda_2)\f]
If it is a greater than a threshold value, it is considered as a corner. If we plot it in
\f$\lambda_1 - \lambda_2\f$ space as we did in Harris Corner Detector, we get an image as below:
@ -28,7 +28,7 @@ If it is a greater than a threshold value, it is considered as a corner. If we p
![image](images/shitomasi_space.png)
From the figure, you can see that only when \f$\lambda_1\f$ and \f$\lambda_2\f$ are above a minimum value,
\f$\lambda_{min}\f$, it is considered as a corner(green region).
\f$\lambda_{\min}\f$, it is considered as a corner(green region).
Code
----

2
doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.markdown
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@ -156,7 +156,7 @@ sift = cv.SIFT_create()
kp, des = sift.detectAndCompute(gray,None)
@endcode
Here kp will be a list of keypoints and des is a numpy array of shape
\f$Number\_of\_Keypoints \times 128\f$.
\f$\text{(Number of Keypoints)} \times 128\f$.
So we got keypoints, descriptors etc. Now we want to see how to match keypoints in different images.
That we will learn in coming chapters.