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classifying images based on their object measurement properties and labeling them as such, how can I do this?

5 vues (au cours des 30 derniers jours)
The following code is able to measure properties of the images read in using regionprops and save them to a features.mat file:
% Calculate features for this zone
numObjects = cc.NumObjects;
if numObjects > 0
areas = [statscc.Area];
profileCounts(k) = numObjects;
totalArea(k) = sum(areas)
zoneArea(k) = areaOfEachZone(k);
avgSize(k) = mean(areas)
circularities = [validObjects];
avgCircularity(k) = mean(circularities(isfinite(circularities)));
ferets = [statscc.MajorAxisLength];
avgFeret(k) = mean(ferets)
minFerets = [statscc.MinorAxisLength];
avgMinFeret(k) = mean(minFerets)
% save features to a structure
features(m).filename = allFileNames{m};
features(m).totalArea = totalArea;
features(m).avgSize = avgSize;
features(m).zoneArea = zoneArea;
features(m).AvgCircularityy = avgCircularity;
features(m).AvgFeret = avgFeret;
features(m).AvgMinFeret = avgMinFeret;
features(m).profileCounts = profileCounts;
however, I want to train this code to discern between images with similar or different features and be able to display "this is a control iamge this is a non control image etc." How can i do this? I have no idea how to approach this. But i have tried other ways and can't reach a conclusion. Can you please help? Thanks!

Réponse acceptée

Image Analyst
Image Analyst le 28 Mar 2023
It's a generic, general purpose demo of how to threshold an image to find blobs, and then measure things about the blobs, and extract certain blobs based on their areas or diameters. For example I show you how to extract different denomination coins based on their diameter.
  2 commentaires
Laura le 28 Mar 2023
Thanks for this, so my question is after this step, how can i train the demo to group the first set of images as one group then use that group to classify theother images read in to that group or a different group? Thanks!
Image Analyst
Image Analyst le 28 Mar 2023
If you'll just try the demo and look at the source code you'll see how to do it. Here is the code. You'll see in this well commented code how I got an image of just the dimes:
% Now I'll demonstrate how to select certain blobs based using the ismember() function and extract them into new subimages.
% Let's say that we wanted to find only those blobs
% with an intensity between 150 and 220 and an area less than 2000 pixels.
% This would give us the three brightest dimes (the smaller coin type).
allBlobIntensities = [props.MeanIntensity];
allBlobAreas = [props.Area];
subplot(3, 3, 7);
% Get a list of the blobs that meet our criteria and we need to keep.
% These will be logical indices - lists of true or false depending on whether the feature meets the criteria or not.
% for example [1, 0, 0, 1, 1, 0, 1, .....]. Elements 1, 4, 5, 7, ... are true, others are false.
allowableIntensityIndexes = (allBlobIntensities > 150) & (allBlobIntensities < 220);
allowableAreaIndexes = allBlobAreas < 2000; % Take the small objects.
% Now let's get actual indexes, rather than logical indexes, of the features that meet the criteria.
% for example [1, 4, 5, 7, .....] to continue using the example from above.
keeperIndexes = find(allowableIntensityIndexes & allowableAreaIndexes);
% Extract only those blobs that meet our criteria, and
% eliminate those blobs that don't meet our criteria.
% Note how we use ismember() to do this. Result will be an image - the same as labeledImage but with only the blobs listed in keeperIndexes in it.
keeperBlobsImage = ismember(labeledImage, keeperIndexes);
% Re-label with only the keeper blobs kept.
labeledDimeImage = bwlabel(keeperBlobsImage, 8); % Label each blob so we can make measurements of it
% Now we're done. We have a labeled image of blobs that meet our specified criteria.
subplot(3, 3, 7);
imshow(labeledDimeImage, []);
axis image;
title('"Keeper" blobs (3 brightest dimes in a re-labeled image)', 'FontSize', captionFontSize);
elapsedTime = toc;
fprintf('Blob detection and measurement took %.3f seconds.\n', elapsedTime)

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