StackOverflow2013

Note that there are some explanatory texts on larger screens.

plurals

POHow to make normalized cross correlation robust to small changes in uniform regions
text
Body
copied!<p>the problem is described below:</p> <p>Given 2 sets of data: A= { 91 87 85 85 84 90 85 83 86 86 90 86 84 89 93 87 89 91 95 97 91 92 97 101 101 },</p> <p>B = {133 130 129 131 133 136 131 131 135 135 133 133 133 131 135 131 129 131 132 132 130 127 129 137 134 },</p> <p>If A represent a set of pixels from a background image around (x,y) location, B represents another set of pixels around (x,y) from different image where the illumination changes. </p> <p>The normalised cross correlation (NCC) calculated = 0.184138251 (from <a href="http://en.wikipedia.org/wiki/Cross-correlation#Normalized_cross-correlation" rel="nofollow">http://en.wikipedia.org/wiki/Cross-correlation#Normalized_cross-correlation</a>)</p> <p>Calculated NCC tells us the set A is different to set B. But in fact, A and B are the same group of pixels under the different illumination conditions.</p> <p>It shows that NCC is very sensitive to the small changes in the data set whose relative variation is quite small. For example, if the ratio between standard deviation and mean is representing the relative variation in each data set, then the relative variation in set A = 0.057684745, in set B = 0.018484007.</p> <p>Could anyone help me to figure out how to incorporate the relative variation factor in NCC formula, so the modified NCC is robust to the small changes in data sets where the variation within each set are very small? Also, the output of modified NCC still needs to be -1 to 1. </p> <p>Thanks a lot.</p>

Querying!

Guidance

An individual column

Larger individual text columns get their own page to allow for proper reading.

SQuiL has stopped working due to an internal error.

If you are curious you may find further information in the browser console, which is accessible through the devtools (F12).

Reload