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前往新版Gitcode,体验更适合开发者的 AI 搜索 >>
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81567a9d
编写于
5月 26, 2021
作者:
H
hyrodium
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电子邮件补丁
差异文件
fix latex script in the docs
上级
4a2adba8
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3
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+7
-7
doc/py_tutorials/py_feature2d/py_features_harris/py_features_harris.markdown
..._feature2d/py_features_harris/py_features_harris.markdown
+4
-4
doc/py_tutorials/py_feature2d/py_shi_tomasi/py_shi_tomasi.markdown
...torials/py_feature2d/py_shi_tomasi/py_shi_tomasi.markdown
+2
-2
doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.markdown
...torials/py_feature2d/py_sift_intro/py_sift_intro.markdown
+1
-1
未找到文件。
doc/py_tutorials/py_feature2d/py_features_harris/py_features_harris.markdown
浏览文件 @
81567a9d
...
...
@@ -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 =
\d
et(M) - k(
\o
peratorname{trace}
(M))^2
\f
]
where
-
\f
$det(M) =
\l
ambda_1
\l
ambda_2
\f
$
-
\f
$
trace
(M) =
\l
ambda_1 +
\l
ambda_2
\f
$
-
\f
$
\l
ambda_1
\f
$ and
\f
$
\l
ambda_2
\f
$ are the eigenvalues of
M
-
\f
$
\
d
et(M) =
\l
ambda_1
\l
ambda_2
\f
$
-
\f
$
\o
peratorname{trace}
(M) =
\l
ambda_1 +
\l
ambda_2
\f
$
-
\f
$
\l
ambda_1
\f
$ and
\f
$
\l
ambda_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.
...
...
doc/py_tutorials/py_feature2d/py_shi_tomasi/py_shi_tomasi.markdown
浏览文件 @
81567a9d
...
...
@@ -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(
\l
ambda_1,
\l
ambda_2)
\f
]
\f
[R =
\
m
in(
\l
ambda_1,
\l
ambda_2)
\f
]
If it is a greater than a threshold value, it is considered as a corner. If we plot it in
\f
$
\l
ambda_1 -
\l
ambda_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
$
\l
ambda_1
\f
$ and
\f
$
\l
ambda_2
\f
$ are above a minimum value,
\f
$
\l
ambda_{min}
\f
$, it is considered as a corner(green region).
\f
$
\l
ambda_{
\
m
in}
\f
$, it is considered as a corner(green region).
Code
----
...
...
doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.markdown
浏览文件 @
81567a9d
...
...
@@ -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
\t
imes 128
\f
$.
\f
$
\t
ext{(Number of Keypoints)}
\t
imes 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.
...
...
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