diff --git a/doc/tutorials/imgproc/histograms/template_matching/images/Template_Matching_Mask_Example.jpg b/doc/tutorials/imgproc/histograms/template_matching/images/Template_Matching_Mask_Example.jpg
new file mode 100644
index 000000000..711faec3b
Binary files /dev/null and b/doc/tutorials/imgproc/histograms/template_matching/images/Template_Matching_Mask_Example.jpg differ
diff --git a/doc/tutorials/imgproc/histograms/template_matching/template_matching.markdown b/doc/tutorials/imgproc/histograms/template_matching/template_matching.markdown
index c39fd159f..5196f424a 100644
--- a/doc/tutorials/imgproc/histograms/template_matching/template_matching.markdown
+++ b/doc/tutorials/imgproc/histograms/template_matching/template_matching.markdown
@@ -19,6 +19,10 @@ Theory
 Template matching is a technique for finding areas of an image that match (are similar) to a
 template image (patch).
 
+While the patch must be a rectangle it may be that not all of the
+rectangle is relevent.  In such a case, a mask can be used to isolate the portion of the patch
+that should be used to find the match.
+
 ### How does it work?
 
 -   We need two primary components:
@@ -51,6 +55,28 @@ template image (patch).
 -   In practice, we use the function @ref cv::minMaxLoc to locate the highest value (or lower,
     depending of the type of matching method) in the *R* matrix.
 
+### How does the mask work?
+- If masking is needed for the match, three components are required:
+
+    -#  **Source image (I):** The image in which we expect to find a match to the template image
+    -#  **Template image (T):** The patch image which will be compared to the template image
+    -#  **Mask image (M):** The mask, a greyscale image that masks the template
+
+
+-   Only two matching methods currently accept a mask: CV_TM_SQDIFF and CV_TM_CCORR_NORMED (see
+    below for explanation of all the matching methods available in opencv).
+
+
+-   The mask must have the same dimensions as the template
+
+
+-   The mask should be a greyscale image where each pixel contains some value from black to white.
+    Pixels that are white are fully included in calculating the best match.  Pixels that are black
+    are excluded from the match.  A value between black and white will include some of
+    the match proportion to how dark the pixel is.
+
+    ![](images/Template_Matching_Mask_Example.jpg)
+
 ### Which are the matching methods available in OpenCV?
 
 Good question. OpenCV implements Template matching in the function @ref cv::matchTemplate . The
@@ -88,10 +114,11 @@ Code
 ----
 
 -   **What does this program do?**
-    -   Loads an input image and a image patch (*template*)
+    -   Loads an input image, an image patch (*template*), and optionally a mask
     -   Perform a template matching procedure by using the OpenCV function @ref cv::matchTemplate
         with any of the 6 matching methods described before. The user can choose the method by
-        entering its selection in the Trackbar.
+        entering its selection in the Trackbar.  If a mask is supplied, it will only be used for
+        the methods that support masking
     -   Normalize the output of the matching procedure
     -   Localize the location with higher matching probability
     -   Draw a rectangle around the area corresponding to the highest match
@@ -115,8 +142,8 @@ Explanation
     @endcode
 -#  Load the source image and template:
     @code{.cpp}
-    img = imread( argv[1], 1 );
-    templ = imread( argv[2], 1 );
+    img = imread( argv[1], IMREAD_COLOR );
+    templ = imread( argv[2], IMREAD_COLOR );
     @endcode
 -#  Create the windows to show the results:
     @code{.cpp}
@@ -150,10 +177,14 @@ Explanation
     @endcode
 -#  Perform the template matching operation:
     @code{.cpp}
-    matchTemplate( img, templ, result, match_method );
+    bool method_accepts_mask = CV_TM_SQDIFF == match_method || match_method == CV_TM_CCORR_NORMED;
+    if (use_mask && method_accepts_mask)
+        { matchTemplate( img, templ, result, match_method, mask); }
+    else
+        { matchTemplate( img, templ, result, match_method); }
     @endcode
-    the arguments are naturally the input image **I**, the template **T**, the result **R** and the
-    match_method (given by the Trackbar)
+    the arguments are naturally the input image **I**, the template **T**, the result **R**, the
+    match_method (given by the Trackbar), and optionally the mask image **M**
 
 -#  We normalize the results:
     @code{.cpp}