From: Vadim Pisarevsky <no@email>
Date: Sun, 19 Jun 2011 23:24:01 +0000 (+0000)
Subject: fixed formula in cvIntegral/integral description
X-Git-Tag: accepted/2.0/20130307.220821~2699
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=2e54482de9e30848436d461577351d89430dddfd;p=profile%2Fivi%2Fopencv.git

fixed formula in cvIntegral/integral description
---

diff --git a/doc/opencv1/c/imgproc_miscellaneous_image_transformations.rst b/doc/opencv1/c/imgproc_miscellaneous_image_transformations.rst
index 3b4819d..11a964b 100644
--- a/doc/opencv1/c/imgproc_miscellaneous_image_transformations.rst
+++ b/doc/opencv1/c/imgproc_miscellaneous_image_transformations.rst
@@ -1116,7 +1116,7 @@ Using these integral images, one may calculate sum, mean and standard deviation
 
 .. math::
 
-    \sum _{x_1<=x<x_2,  \, y_1<=y<y_2} =  \texttt{sum} (x_2,y_2)- \texttt{sum} (x_1,y_2)- \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,x_1) 
+    \sum _{x_1<=x<x_2,  \, y_1<=y<y_2} =  \texttt{sum} (x_2,y_2)- \texttt{sum} (x_1,y_2)- \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,y_1) 
 
 
 It makes possible to do a fast blurring or fast block correlation with variable window size, for example. In the case of multi-channel images, sums for each channel are accumulated independently.
diff --git a/modules/imgproc/doc/miscellaneous_transformations.rst b/modules/imgproc/doc/miscellaneous_transformations.rst
index 24741e4..0bceaee 100644
--- a/modules/imgproc/doc/miscellaneous_transformations.rst
+++ b/modules/imgproc/doc/miscellaneous_transformations.rst
@@ -634,7 +634,7 @@ Using these integral images, you can calculate sum, mean and standard deviation
 
 .. math::
 
-    \sum _{x_1 \leq x < x_2,  \, y_1  \leq y < y_2}  \texttt{image} (x,y) =  \texttt{sum} (x_2,y_2)- \texttt{sum} (x_1,y_2)- \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,x_1)
+    \sum _{x_1 \leq x < x_2,  \, y_1  \leq y < y_2}  \texttt{image} (x,y) =  \texttt{sum} (x_2,y_2)- \texttt{sum} (x_1,y_2)- \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,y_1)
 
 It makes possible to do a fast blurring or fast block correlation with a variable window size, for example. In case of multi-channel images, sums for each channel are accumulated independently.