Merge remote-tracking branch 'upstream/3.4' into merge-3.4

doc/js_tutorials/js_gui/js_image_display/js_image_display.markdown

@@ -13,7 +13,7 @@ OpenCV.js saves images as cv.Mat type. We use HTML canvas element to transfer cv
 or in reverse. The ImageData interface can represent or set the underlying pixel data of an area of a
 canvas element.
 
-@sa Please refer to canvas docs for more details.
+@note Please refer to canvas docs for more details.
 
 First, create an ImageData obj from canvas:
 @code{.js}

doc/py_tutorials/py_calib3d/py_calibration/py_calibration.markdown

@@ -83,7 +83,7 @@ use 7x6 grid. (Normally a chess board has 8x8 squares and 7x7 internal corners).
 corner points and retval which will be True if pattern is obtained. These corners will be placed in
 an order (from left-to-right, top-to-bottom)
 
-@sa This function may not be able to find the required pattern in all the images. So, one good option
+@note This function may not be able to find the required pattern in all the images. So, one good option
 is to write the code such that, it starts the camera and check each frame for required pattern. Once
 the pattern is obtained, find the corners and store it in a list. Also, provide some interval before
 reading next frame so that we can adjust our chess board in different direction. Continue this
@@ -91,7 +91,7 @@ process until the required number of good patterns are obtained. Even in the exa
 are not sure how many images out of the 14 given are good.  Thus, we must read all the images and take only the good
 ones.
 
-@sa Instead of chess board, we can alternatively use a circular grid.  In this case, we must use the function
+@note Instead of chess board, we can alternatively use a circular grid.  In this case, we must use the function
 **cv.findCirclesGrid()** to find the pattern. Fewer images are sufficient to perform camera calibration using a circular grid.
 
 Once we find the corners, we can increase their accuracy using **cv.cornerSubPix()**. We can also

doc/py_tutorials/py_gui/py_image_display/py_image_display.markdown

@@ -132,7 +132,7 @@ A screen-shot of the window will look like this :
 
 ![image](images/matplotlib_screenshot.jpg)
 
-@sa Plenty of plotting options are available in Matplotlib. Please refer to Matplotlib docs for more
+@note Plenty of plotting options are available in Matplotlib. Please refer to Matplotlib docs for more
 details. Some, we will see on the way.
 
 __warning__

doc/py_tutorials/py_imgproc/py_contours/py_contours_more_functions/py_contours_more_functions.markdown

@@ -113,7 +113,7 @@ I got following results:
 
 See, even image rotation doesn't affect much on this comparison.
 
-@sa [Hu-Moments](http://en.wikipedia.org/wiki/Image_moment#Rotation_invariant_moments) are seven
+@note [Hu-Moments](http://en.wikipedia.org/wiki/Image_moment#Rotation_invariant_moments) are seven
 moments invariant to translation, rotation and scale. Seventh one is skew-invariant. Those values
 can be found using **cv.HuMoments()** function.
 

doc/py_tutorials/py_imgproc/py_histograms/py_histogram_begins/py_histogram_begins.markdown

@@ -94,7 +94,7 @@ hist is same as we calculated before. But bins will have 257 elements, because N
 as 0-0.99, 1-1.99, 2-2.99 etc. So final range would be 255-255.99. To represent that, they also add
 256 at end of bins. But we don't need that 256. Upto 255 is sufficient.
 
-@sa Numpy has another function, **np.bincount()** which is much faster than (around 10X)
+@note Numpy has another function, **np.bincount()** which is much faster than (around 10X)
 np.histogram(). So for one-dimensional histograms, you can better try that. Don't forget to set
 minlength = 256 in np.bincount. For example, hist = np.bincount(img.ravel(),minlength=256)
 

doc/tutorials/ml/introduction_to_svm/introduction_to_svm.markdown

@@ -51,7 +51,7 @@ Let's introduce the notation used to define formally a hyperplane:
 
 where \f$\beta\f$ is known as the *weight vector* and \f$\beta_{0}\f$ as the *bias*.
 
-@sa A more in depth description of this and hyperplanes you can find in the section 4.5 (*Separating
+@note A more in depth description of this and hyperplanes you can find in the section 4.5 (*Separating
 Hyperplanes*) of the book: *Elements of Statistical Learning* by T. Hastie, R. Tibshirani and J. H.
 Friedman (@cite HTF01).
 

doc/tutorials/videoio/video-input-psnr-ssim/video_input_psnr_ssim.markdown

@@ -164,7 +164,7 @@ Describing the methods goes well beyond the purpose of this tutorial. For that I
 the article introducing it. Nevertheless, you can get a good image of it by looking at the OpenCV
 implementation below.
 
-@sa
+@note
     SSIM is described more in-depth in the: "Z. Wang, A. C. Bovik, H. R. Sheikh and E. P.
     Simoncelli, "Image quality assessment: From error visibility to structural similarity," IEEE
     Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, Apr. 2004." article.