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

doc/js_tutorials/js_imgproc/js_gradients/js_gradients.markdown

@@ -15,7 +15,7 @@ We will see each one of them.
 
 ### 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
 (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

doc/py_tutorials/py_imgproc/py_gradients/py_gradients.markdown

@@ -17,7 +17,7 @@ We will see each one of them.
 
 ### 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
 (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

modules/video/src/bgfg_gaussmix2.cpp

@@ -47,7 +47,7 @@
 //International Conference Pattern Recognition, UK, August, 2004
 //http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf
 //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
 //
@@ -97,7 +97,7 @@ namespace cv
  http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf
 
  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)
 
 */