diff --git a/doc/tutorials/imgproc/imgtrans/filter_2d/filter_2d.rst b/doc/tutorials/imgproc/imgtrans/filter_2d/filter_2d.rst
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+.. _filter_2d:
+
+Making your own linear filters!
+********************************
+
+Goal
+=====
+
+In this tutorial you will learn how to:
+
+* Use the OpenCV function :filter2d:`filter2D <>` to create your own linear filters.
+
+Theory
+============
+
+.. note::
+ The explanation below belongs to the book **Learning OpenCV** by Bradski and Kaehler.
+
+
+Convolution
+------------
+In a very general sense, convolution is an operation between every part of an image and an operator (kernel).
+
+What is a kernel?
+------------------
+A kernel is essentially a fixed size array of numerical coefficeints along with an *anchor point* in that array, which is tipically located at the center.
+
+.. image:: images/filter_2d_tutorial_kernel_theory.png
+ :alt: kernel example
+ :align: center
+
+How does convolution with a kernel work?
+-----------------------------------------
+
+Assume you want to know the resulting value of a particular location in the image. The value of the convolution is calculated in the following way:
+
+#. Place the kernel anchor on top of a determined pixel, with the rest of the kernel overlaying the corresponding local pixels in the image.
+
+#. Multiply the kernel coefficients by the corresponding image pixel values and sum the result.
+
+#. Place the result to the location of the *anchor* in the input image.
+
+#. Repeat the process for all pixels by scanning the kernel over the entire image.
+
+Expressing the procedure above in the form of an equation we would have:
+
+.. math::
+
+ H(x,y) = \sum_{i=0}^{M_{i} - 1} \sum_{j=0}^{M_{j}-1} I(x+i - a_{i}, y + j - a_{j})K(i,j)
+
+Fortunately, OpenCV provides you with the function :filter2d:`filter2D <>` so you do not have to code all these operations.
+
+Code
+======
+
+#. **What does this program do?**
+
+ * Loads an image
+ * Performs a *normalized box filter*. For instance, for a kernel of size :math:`size = 3`, the kernel would be:
+
+ .. math::
+
+ K = \dfrac{1}{3 \cdot 3} \begin{bmatrix}
+ 1 & 1 & 1 \\
+ 1 & 1 & 1 \\
+ 1 & 1 & 1
+ \end{bmatrix}
+
+ The program will perform the filter operation with kernels of sizes 3, 5, 7, 9 and 11.
+
+ * The filter output (with each kernel) will be shown during 500 milliseconds
+
+#. The tutorial code's is shown lines below. You can also download it from `here <https://code.ros.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/ImgTrans/filter2D_demo.cpp>`_
+
+
+.. code-block:: cpp
+
+ #include "opencv2/imgproc/imgproc.hpp"
+ #include "opencv2/highgui/highgui.hpp"
+ #include <stdlib.h>
+ #include <stdio.h>
+
+ using namespace cv;
+
+ /** @function main */
+ int main ( int argc, char** argv )
+ {
+ /// Declare variables
+ Mat src, dst;
+
+ Mat kernel;
+ Point anchor;
+ double delta;
+ int ddepth;
+ int kernel_size;
+ char* window_name = "filter2D Demo";
+
+ int c;
+
+ /// Load an image
+ src = imread( argv[1] );
+
+ if( !src.data )
+ { return -1; }
+
+ /// Create window
+ namedWindow( window_name, CV_WINDOW_AUTOSIZE );
+
+ /// Initialize arguments for the filter
+ anchor = Point( -1, -1 );
+ delta = 0;
+ ddepth = -1;
+
+ /// Loop - Will filter the image with different kernel sizes each 0.5 seconds
+ int ind = 0;
+ while( true )
+ {
+ c = waitKey(500);
+ /// Press 'ESC' to exit the program
+ if( (char)c == 27 )
+ { break; }
+
+ /// Update kernel size for a normalized box filter
+ kernel_size = 3 + 2*( ind%5 );
+ kernel = Mat::ones( kernel_size, kernel_size, CV_32F )/ (float)(kernel_size*kernel_size);
+
+ /// Apply filter
+ filter2D(src, dst, ddepth , kernel, anchor, delta, BORDER_DEFAULT );
+ imshow( window_name, dst );
+ ind++;
+ }
+
+ return 0;
+ }
+Explanation
+=============
+
+#. We begin with the usual steps:
+
+ * Load an image
+
+ .. code-block:: cpp
+
+ src = imread( argv[1] );
+
+ if( !src.data )
+ { return -1; }
+
+ * Create a window to display the result
+
+ .. code-block:: cpp
+
+ namedWindow( window_name, CV_WINDOW_AUTOSIZE );
+
+#. Initialize the arguments for the linear filter
+
+ .. code-block:: cpp
+
+ anchor = Point( -1, -1 );
+ delta = 0;
+ ddepth = -1;
+
+
+#. Perform an infinite loop updating the kernel size and applying our linear filter to the input image. Let's analyze that more in detail:
+
+#. First we define the kernel our filter is going to use. Here it is:
+
+ .. code-block:: cpp
+
+ kernel_size = 3 + 2*( ind%5 );
+ kernel = Mat::ones( kernel_size, kernel_size, CV_32F )/ (float)(kernel_size*kernel_size);
+
+ The first line is to update the *kernel_size* to odd values in the range: :math:`[3,11]`. The second line actually builds the kernel by setting its value to a matrix filled with :math:`1's` and normalizing it by dividing it between the number of elements.
+
+#. After setting the kernel, we can generate the filter by using the function :filter2d:`filter2D <>`:
+
+ .. code-block:: cpp
+
+ filter2D(src, dst, ddepth , kernel, anchor, delta, BORDER_DEFAULT );
+
+ The arguments denote:
+
+ a. *src*: Source image
+ #. *dst*: Destination image
+ #. *ddepth*: The depth of *dst*. A negative value (such as :math:`-1`) indicates that the depth is the same as the source.
+ #. *kernel*: The kernel to be scanned through the image
+ #. *anchor*: The position of the anchor relative to its kernel. The location *Point(-1, -1)* indicates the center by default.
+ #. *delta*: A value to be added to each pixel during the convolution. By default it is :math:`0`
+ #. *BORDER_DEFAULT*: We let this value by default (more details in the following tutorial)
+
+#. Our program will effectuate a *while* loop, each 500 ms the kernel size of our filter will be updated in the range indicated.
+
+Results
+========
+
+#. After compiling the code above, you can execute it giving as argument the path of an image. The result should be a window that shows an image blurred by a normalized filter. Each 0.5 seconds the kernel size should change, as can be seen in the series of snapshots below:
+
+ .. image:: images/filter_2d_tutorial_result.png
+ :alt: kernel example
+ :align: center
diff --git a/doc/tutorials/imgproc/imgtrans/filter_2d/images/filter_2d_tutorial_kernel_theory.png b/doc/tutorials/imgproc/imgtrans/filter_2d/images/filter_2d_tutorial_kernel_theory.png
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diff --git a/doc/tutorials/imgproc/table_of_content_imgproc/images/imgtrans/Filter_2D_Tutorial_Cover.jpg b/doc/tutorials/imgproc/table_of_content_imgproc/images/imgtrans/Filter_2D_Tutorial_Cover.jpg
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diff --git a/doc/tutorials/imgproc/table_of_content_imgproc/table_of_content_imgproc.rst b/doc/tutorials/imgproc/table_of_content_imgproc/table_of_content_imgproc.rst
index 2f19080eca..5c9621c658 100644
--- a/doc/tutorials/imgproc/table_of_content_imgproc/table_of_content_imgproc.rst
+++ b/doc/tutorials/imgproc/table_of_content_imgproc/table_of_content_imgproc.rst
@@ -103,3 +103,26 @@ In this section you will learn about the image processing (manipulation) functio
.. |Threshold| image:: images/Threshold_Tutorial_Cover.png
:height: 100pt
:width: 100pt
+
+.. ************************
+.. ImgTrans
+.. ************************
+
+* :ref:`filter_2d`
+
+ ===================== ==============================================
+ |Filter_2D| *Title:* **Making your own linear filters**
+
+ *Compatibility:* > OpenCV 2.0
+
+ *Author:* |Author_AnaH|
+
+ Where we learn to design our own filters by using OpenCV functions
+
+ ===================== ==============================================
+
+ .. |Filter_2D| image:: images/imgtrans/Filter_2D_Tutorial_Cover.jpg
+ :height: 100pt
+ :width: 100pt
+
+