### 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
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### 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
//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
//
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)
*/