Normalized standard deviation
What is the easiest way to calculate normalized standard deviation for a certain region of an image?
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you could use meanStdDev for this.
Mat m(5,5,CV_8U);
randu(m,0,100);
Rect roi(2,2,2,2);
cerr << m << endl;
cerr << m(roi) << endl;
Scalar mea,dev;
meanStdDev( m(roi), mea, dev );
cerr << mea << endl;
cerr << dev << endl;
[ 6, 97, 39, 29, 97;
10, 86, 93, 76, 29;
51, 38, 7, 38, 75;
23, 18, 1, 17, 3;
53, 43, 75, 64, 48]
[ 7, 38;
1, 17]
[15.75, 0, 0, 0]
[14.0601, 0, 0, 0]
Asked: 2015-01-16 02:39:31 -0600
Seen: 1,820 times
Last updated: Jan 16 '15
Don't you want to say normalized by standard deviation? see this for how to do it. You can also use normalize for normalization, but I am agraid that I am not really understanding your question. Maybe it is a normalization first (if no values, than it's between 0 and 1) and then compute
meanStdDevIn the meantime, I looked up the exact definition which I hadn't known before, that's why I asked the question. In fact, it is only a division by the squared mean.