issue when quantifying logo symmetry
1 visualización (últimos 30 días)
Mostrar comentarios más antiguos
Sebano
el 19 de Mayo de 2020
Comentada: Tengbo Zhuang
el 2 de Jul. de 2020
Hi, I am trying to quantify the symmetry of logo images. So far, I have used the "immse" (mean square error) function and the "fliplr" in MatLab to compare the differences in mean square error (MSE) between the originial logo and the flipped version of the logo to quantify the symmetry. I am experiencing one issue atm with this approach and I need your help... When analysing logos that are 100% symmetric, the MSE can sometimes be super high when it should be extremely close to zero, since theres no differences between the original and flipped version. What may the issue be here? and any ideas on how to solve it?
4 comentarios
Respuesta aceptada
Walter Roberson
el 23 de Mayo de 2020
regionprops(double(grayimage>0), 'centroid')
and observe that the centroid is 1 pixel to the right of where you would expect.
So construct a new image that is padded on the right with one column of 0. Run the immse and you will see a lower value.
I recommend that you imshow(imsubtract()) the two images to see visually how they are not quite symmetric. As I discussed earlier some of the problem is jpg blurring of edges.
0 comentarios
Más respuestas (1)
Image Analyst
el 23 de Mayo de 2020
Editada: Image Analyst
el 23 de Mayo de 2020
You have to make sure you're truly flipping about the true vertical axis, which I suspect you're not. You'd have to get rid of the TM in the Starbucks logo that is making the whole logo not symmetric.
Usually symmetry is computed using a symmetry metric like the Sørensen–Dice coefficient.
See attached demo.
11 comentarios
Image Analyst
el 2 de Jul. de 2020
It was in the file:
%===============================================================================
% Get the thresholds from which we'll use to create two binary images:
function [lowThreshold, highThreshold] = GetThresholds(pixelCount, grayLevels)
cdf = cumsum(pixelCount);
cdf = cdf / cdf(end);
lowPercentage = 20;
lowThresholdIndex = find(cdf > lowPercentage / 100, 1, 'first');
lowThreshold = grayLevels(lowThresholdIndex);
highPercentage = 40;
highThresholdIndex = find(cdf > highPercentage / 100, 1, 'first');
highThreshold = grayLevels(highThresholdIndex);
Looks like you changed the name when you called it from GetThresholds to GetThersholds.
Tengbo Zhuang
el 2 de Jul. de 2020
Right! Didn't notice that
I just tested Apple, I think it works now! (0.85) Thanks a lot!
Ver también
Categorías
Más información sobre Historical Contests en Help Center y File Exchange.
Productos
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!