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Merge pull request #17123 from ganesh-k13:bugfix/documentation/3.4/16987

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Alexander Alekhin 2020-04-21 21:03:35 +00:00
commit 775d031366
4 changed files with 6 additions and 6 deletions
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2
doc/tutorials/ml/non_linear_svms/non_linear_svms.markdown
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@ -43,7 +43,7 @@ There are multiple ways in which this model can be modified so it takes into acc
misclassification errors. For example, one could think of minimizing the same quantity plus a misclassification errors. For example, one could think of minimizing the same quantity plus a
constant times the number of misclassification errors in the training data, i.e.: constant times the number of misclassification errors in the training data, i.e.:
\f[\min ||\beta||^{2} + C \text{(\# misclassication errors)}\f] \f[\min ||\beta||^{2} + C \text{(misclassification errors)}\f]
However, this one is not a very good solution since, among some other reasons, we do not distinguish However, this one is not a very good solution since, among some other reasons, we do not distinguish
between samples that are misclassified with a small distance to their appropriate decision region or between samples that are misclassified with a small distance to their appropriate decision region or

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modules/calib3d/include/opencv2/calib3d.hpp
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@ -1760,7 +1760,7 @@ Optionally, it computes the essential matrix E:
where \f$T_i\f$ are components of the translation vector \f$T\f$ : \f$T=[T_0, T_1, T_2]^T\f$ . where \f$T_i\f$ are components of the translation vector \f$T\f$ : \f$T=[T_0, T_1, T_2]^T\f$ .
And the function can also compute the fundamental matrix F: And the function can also compute the fundamental matrix F:
\f[F = cameraMatrix2^{-T} E cameraMatrix1^{-1}\f] \f[F = cameraMatrix2^{-T}\cdot E \cdot cameraMatrix1^{-1}\f]
Besides the stereo-related information, the function can also perform a full calibration of each of Besides the stereo-related information, the function can also perform a full calibration of each of
the two cameras. However, due to the high dimensionality of the parameter space and noise in the the two cameras. However, due to the high dimensionality of the parameter space and noise in the

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modules/imgproc/include/opencv2/imgproc.hpp
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@ -226,7 +226,7 @@ enum MorphTypes{
enum MorphShapes { enum MorphShapes {
MORPH_RECT = 0, //!< a rectangular structuring element: \f[E_{ij}=1\f] MORPH_RECT = 0, //!< a rectangular structuring element: \f[E_{ij}=1\f]
MORPH_CROSS = 1, //!< a cross-shaped structuring element: MORPH_CROSS = 1, //!< a cross-shaped structuring element:
//!< \f[E_{ij} = \fork{1}{if i=\texttt{anchor.y} or j=\texttt{anchor.x}}{0}{otherwise}\f] //!< \f[E_{ij} = \begin{cases} 1 & \texttt{if } {i=\texttt{anchor.y } {or } {j=\texttt{anchor.x}}} \\0 & \texttt{otherwise} \end{cases}\f]
MORPH_ELLIPSE = 2 //!< an elliptic structuring element, that is, a filled ellipse inscribed MORPH_ELLIPSE = 2 //!< an elliptic structuring element, that is, a filled ellipse inscribed
//!< into the rectangle Rect(0, 0, esize.width, 0.esize.height) //!< into the rectangle Rect(0, 0, esize.width, 0.esize.height)
}; };
@ -1457,7 +1457,7 @@ The function smooths an image using the kernel:
where where
\f[\alpha = \fork{\frac{1}{\texttt{ksize.width*ksize.height}}}{when \texttt{normalize=true}}{1}{otherwise}\f] \f[\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} & \texttt{when } \texttt{normalize=true} \\1 & \texttt{otherwise}\end{cases}\f]
Unnormalized box filter is useful for computing various integral characteristics over each pixel Unnormalized box filter is useful for computing various integral characteristics over each pixel
neighborhood, such as covariance matrices of image derivatives (used in dense optical flow neighborhood, such as covariance matrices of image derivatives (used in dense optical flow
@ -1531,7 +1531,7 @@ according to the specified border mode.
The function does actually compute correlation, not the convolution: The function does actually compute correlation, not the convolution:
\f[\texttt{dst} (x,y) = \sum _{ \stackrel{0\leq x' < \texttt{kernel.cols},}{0\leq y' < \texttt{kernel.rows}} } \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\f] \f[\texttt{dst} (x,y) = \sum _{ \substack{0\leq x' < \texttt{kernel.cols}\\{0\leq y' < \texttt{kernel.rows}}}} \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\f]
That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip
the kernel using #flip and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows - the kernel using #flip and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows -

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modules/video/include/opencv2/video/tracking.hpp
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@ -308,7 +308,7 @@ Default values are shown in the declaration above.
The function estimates the optimum transformation (warpMatrix) with respect to ECC criterion The function estimates the optimum transformation (warpMatrix) with respect to ECC criterion
(@cite EP08), that is (@cite EP08), that is
\f[\texttt{warpMatrix} = \texttt{warpMatrix} = \arg\max_{W} \texttt{ECC}(\texttt{templateImage}(x,y),\texttt{inputImage}(x',y'))\f] \f[\texttt{warpMatrix} = \arg\max_{W} \texttt{ECC}(\texttt{templateImage}(x,y),\texttt{inputImage}(x',y'))\f]
where where