Question

I’d like to know what would be the best strategy to compare a group of contours, in fact are edges resulting of a canny edges detection, from two pictures, in order to know which pair is more alike.

I have this image:

http://i55.tinypic.com/10fe1y8.jpg

And I would like to know how can I calculate which one of these fits best to it:

http://i56.tinypic.com/zmxd13.jpg

(it should be the one on the right)

Is there anyway to compare the contours as a whole?
I can easily rotate the images but I don’t know what functions to use in order to calculate that the reference image on the right is the best fit.

Here it is what I’ve already tried using opencv:

matchShapes function – I tried this function using 2 gray scales images and I always get the same result in every comparison image and the value seems wrong as it is 0,0002.

So what I realized about matchShapes, but I’m not sure it’s the correct assumption, is that the function works with pairs of contours and not full images. Now this is a problem because although I have the contours of the images I want to compare, they are hundreds and I don’t know which ones should be “paired up”.

So I also tried to compare all the contours of the first image against the other two with a for iteration but I might be comparing,for example, the contour of the 5 against the circle contour of the two reference images and not the 2 contour.

Also tried simple cv::compare function and matchTemplate, none with success.

Answer 1

Well, for this you have a couple of options depending on how robust you need your approach to be.

Simple Solutions (with assumptions):

For these methods, I’m assuming your the images you supplied are what you are working with (i.e., the objects are already segmented and approximately the same scale. Also, you will need to correct the rotation (at least in a coarse manner). You might do something like iteratively rotate the comparison image every 10, 30, 60, or 90 degrees, or whatever coarseness you feel you can get away with.

For example,

for(degrees = 10; degrees < 360; degrees += 10)
    coinRot = rotate(compareCoin, degrees)
    // you could also try Cosine Similarity, or even matchedTemplate here.
    metric = SAD(coinRot, targetCoin) 
    if(metric > bestMetric)
        bestMetric = metric
        coinRotation = degrees

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• Sum of Absolute Differences (SAD): This will allow you to quickly compare the images once you have determined an approximate rotation angle.
• Cosine Similarity: This operates a bit differently by treating the image as a 1D vector, and then computes the the high-dimensional angle between the two vectors. The better the match the smaller the angle will be.

Complex Solutions (possibly more robust):

These solutions will be more complex to implement, but will probably yield more robust classifications.

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• Haussdorf Distance: This answer will give you an introduction on using this method. This solution will probably also need the rotation correction to work properly.
• Fourier-Mellin Transform: This method is an extension of Phase Correlation, which can extract the rotation, scale, and translation (RST) transform between two images.
• Feature Detection and Extraction: This method involves detecting “robust” (i.e., scale and/or rotation invariant) features in the image and comparing them against a set of target features with RANSAC, LMedS, or simple least squares. OpenCV has a couple of samples using this technique in matcher_simple.cpp and matching_to_many_images.cpp. NOTE: With this method you will probably not want to binarize the image, so there are more detectable features available.