Merge pull request #22839 from zchrissirhcz:fix-typo-3.4

pull/22842/head
Alexander Alekhin 2 years ago
commit 6c36cd5d6e
  1. 4
      doc/js_tutorials/js_imgproc/js_gradients/js_gradients.markdown
  2. 2
      doc/py_tutorials/py_imgproc/py_gradients/py_gradients.markdown
  3. 4
      modules/video/src/bgfg_gaussmix2.cpp

@ -15,7 +15,7 @@ We will see each one of them.
### 1. Sobel and Scharr Derivatives ### 1. Sobel and Scharr Derivatives
Sobel operators is a joint Gausssian smoothing plus differentiation operation, so it is more Sobel operators is a joint Gaussian smoothing plus differentiation operation, so it is more
resistant to noise. You can specify the direction of derivatives to be taken, vertical or horizontal resistant to noise. You can specify the direction of derivatives to be taken, vertical or horizontal
(by the arguments, yorder and xorder respectively). You can also specify the size of kernel by the (by the arguments, yorder and xorder respectively). You can also specify the size of kernel by the
argument ksize. If ksize = -1, a 3x3 Scharr filter is used which gives better results than 3x3 Sobel argument ksize. If ksize = -1, a 3x3 Scharr filter is used which gives better results than 3x3 Sobel
@ -97,4 +97,4 @@ Try it
<iframe src="../../js_gradients_absSobel.html" width="100%" <iframe src="../../js_gradients_absSobel.html" width="100%"
onload="this.style.height=this.contentDocument.body.scrollHeight +'px';"> onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
</iframe> </iframe>
\endhtmlonly \endhtmlonly

@ -17,7 +17,7 @@ We will see each one of them.
### 1. Sobel and Scharr Derivatives ### 1. Sobel and Scharr Derivatives
Sobel operators is a joint Gausssian smoothing plus differentiation operation, so it is more Sobel operators is a joint Gaussian smoothing plus differentiation operation, so it is more
resistant to noise. You can specify the direction of derivatives to be taken, vertical or horizontal resistant to noise. You can specify the direction of derivatives to be taken, vertical or horizontal
(by the arguments, yorder and xorder respectively). You can also specify the size of kernel by the (by the arguments, yorder and xorder respectively). You can also specify the size of kernel by the
argument ksize. If ksize = -1, a 3x3 Scharr filter is used which gives better results than 3x3 Sobel argument ksize. If ksize = -1, a 3x3 Scharr filter is used which gives better results than 3x3 Sobel

@ -47,7 +47,7 @@
//International Conference Pattern Recognition, UK, August, 2004 //International Conference Pattern Recognition, UK, August, 2004
//http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf //http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf
//The code is very fast and performs also shadow detection. //The code is very fast and performs also shadow detection.
//Number of Gausssian components is adapted per pixel. //Number of Gaussian components is adapted per pixel.
// //
// and // and
// //
@ -97,7 +97,7 @@ namespace cv
http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf
Advantages: Advantages:
-fast - number of Gausssian components is constantly adapted per pixel. -fast - number of Gaussian components is constantly adapted per pixel.
-performs also shadow detection (see bgfg_segm_test.cpp example) -performs also shadow detection (see bgfg_segm_test.cpp example)
*/ */

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