improved look of the generated PDF and html docs; started merging reference manuals

modules/core/doc/operations_on_arrays.rst

@@ -31,6 +31,16 @@ Computes the per-element absolute difference between two arrays or between an ar
 
 .. ocv:function:: void absdiff(InputArray src1, InputArray src2, OutputArray dst)
 
+.. ocv:pyfunction:: cv2.absdiff(src1, src2, [dst=None]) -> dst
+
+.. ocv:pyoldfunction:: cv.AbsDiff(src1, src2, dst) -> None
+
+.. ocv:pyoldfunction:: cv.AbsDiffS(src1, src2, dst) -> None
+
+.. ocv:cfunction:: void cvAbsDiff(const CvArr* src1, const CvArr* src2, CvArr* dst)
+
+.. ocv:cfunction:: void cvAbsDiffS(const CvArr* src1, CvScalar src2, CvArr* dst)
+
     :param src1: First input array or a scalar.
     
     :param src2: Second input array or a scalar.

modules/ml/doc/boosting.rst

@@ -52,7 +52,7 @@ Different variants of boosting are known as Discrete Adaboost, Real AdaBoost, Lo
     3.3. Set :math:`w_i \Leftarrow w_i exp[c_m 1_{(y_i \neq f_m(x_i))}], i = 1,2,...,N,` and renormalize so that :math:`\Sigma i w_i = 1`         .
 
 
-#. Classify new samples *x* using the formula: :math:`\sign (\Sigma m = 1M c_m f_m(x))`         .
+#. Classify new samples *x* using the formula: :math:`\textrm{sign} (\Sigma m = 1M c_m f_m(x))`         .
 
 
 .. note:: Similar to the classical boosting methods, the current implementation supports two-class classifiers only. For M