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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 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. 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 where
- \f$det(M) = \lambda_1 \lambda_2\f$ - \f$\det(M) = \lambda_1 \lambda_2\f$
- \f$trace(M) = \lambda_1 + \lambda_2\f$ - \f$\operatorname{trace}(M) = \lambda_1 + \lambda_2\f$
- \f$\lambda_1\f$ and \f$\lambda_2\f$ are the eigenvalues of M - \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. 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: 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 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: \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) ![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, 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 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) kp, des = sift.detectAndCompute(gray,None)
@endcode @endcode
Here kp will be a list of keypoints and des is a numpy array of shape 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. 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. That we will learn in coming chapters.