updated docs, fixed few bugs

modules/tracking/doc/tracking.rst

-Tracking API
-============
+******************************************
+tracking. Tracking API
+******************************************
 
 .. highlight:: cpp
 

modules/ximgproc/doc/structured_edge_detection.rst

@@ -50,8 +50,8 @@ StructuredEdgeDetection::detectEdges
 
 .. seealso::
 
-    :ocv:class:`Sobel`,
-    :ocv:class:`Canny`
+    :ocv:func:`Sobel`,
+    :ocv:func:`Canny`
 
 createStructuredEdgeDetection
 +++++++++++++++++++++++++++++

modules/ximgproc/doc/ximgproc.rst

@@ -7,5 +7,5 @@ ximgproc. Extended image processing module.
 .. toctree::
     :maxdepth: 2
 
-    edge_aware_filters
     structured_edge_detection
+    edge_aware_filters

modules/xphoto/doc/colorbalance/whitebalance.rst

@@ -5,21 +5,18 @@ Automatic white balance correction
 
 balanceWhite
 ------------
+
 .. ocv:function:: void balanceWhite(const Mat &src, Mat &dst, const int algorithmType, const float inputMin  = 0.0f, const float inputMax  = 255.0f, const float outputMin = 0.0f, const float outputMax = 255.0f)
 
-The function implements different algorithm of automatic white balance, i.e.
-it tries to map image's white color to perceptual white (this can be violated
-due to specific illumination or camera settings).
+	The function implements different algorithm of automatic white balance, i.e. it tries to map image's white color to perceptual white (this can be violated due to specific illumination or camera settings).
 
-    :param src : source image
-    :param dst : destination image
-    :param algorithmType : type of the algorithm to use. Use WHITE_BALANCE_SIMPLE to perform smart histogram adjustments (ignoring 4% pixels with minimal and maximal values) for each channel.
-    :param inputMin : minimum value in the input image
-    :param inputMax : maximum value in the input image
-    :param outputMin : minimum value in the output image
-    :param outputMax : maximum value in the output image
+    :param algorithmType: type of the algorithm to use. Use WHITE_BALANCE_SIMPLE to perform smart histogram adjustments (ignoring 4% pixels with minimal and maximal values) for each channel.
+    :param inputMin: minimum value in the input image
+    :param inputMax: maximum value in the input image
+    :param outputMin: minimum value in the output image
+    :param outputMax: maximum value in the output image
 
 .. seealso::
 
     :ocv:func:`cvtColor`,
-    :ocv:func:`equalizeHist`
\ No newline at end of file
+    :ocv:func:`equalizeHist`

modules/xphoto/doc/denoising/denoising.rst

@@ -7,13 +7,13 @@ dctDenoising
 ------------
 .. ocv:function:: void dctDenoising(const Mat &src, Mat &dst, const float sigma)
 
-The function implements simple dct-based denoising,
-link: http://www.ipol.im/pub/art/2011/ys-dct/.
+	The function implements simple dct-based denoising,
+	link: http://www.ipol.im/pub/art/2011/ys-dct/.
 
-    :param src : source image
-    :param dst : destination image
-    :param sigma : expected noise standard deviation
-    :param psize : size of block side where dct is computed
+    :param src: source image
+    :param dst: destination image
+    :param sigma: expected noise standard deviation
+    :param psize: size of block side where dct is computed
 
 .. seealso::
 

modules/xphoto/doc/inpainting/inpainting.rst

@@ -7,12 +7,12 @@ Inpainting
 ----------
 .. ocv:function:: void inpaint(const Mat &src, const Mat &mask, Mat &dst, const int algorithmType)
 
-The function implements different single-image inpainting algorithms.
+	The function implements different single-image inpainting algorithms.
 
-    :param src : source image, it could be of any type and any number of channels from 1 to 4. In case of 3- and 4-channels images the function expect them in CIELab colorspace or similar one, where first color component shows intensity, while second and third shows colors. Nonetheless you can try any colorspaces.
-    :param mask : mask (CV_8UC1), where non-zero pixels indicate valid image area, while zero pixels indicate area to be inpainted
-    :param dst : destination image
-    :param algorithmType : expected noise standard deviation
+    :param src: source image, it could be of any type and any number of channels from 1 to 4. In case of 3- and 4-channels images the function expect them in CIELab colorspace or similar one, where first color component shows intensity, while second and third shows colors. Nonetheless you can try any colorspaces.
+    :param mask: mask (CV_8UC1), where non-zero pixels indicate valid image area, while zero pixels indicate area to be inpainted
+    :param dst: destination image
+    :param algorithmType: expected noise standard deviation
         * INPAINT_SHIFTMAP: This algorithm searches for dominant correspondences (transformations) of image patches and tries to seamlessly fill-in the area to be inpainted using this transformations. Look in the original paper [He2012]_ for details.
 
     .. [He2012] K. He, J. Sun., "Statistics of Patch Offsets for Image Completion",