Image processing is really a task for C/C++ based tool. There has been a vast ammount of work done on the topic and it gets into tertiary level math. If you wanted to do it in Perl/CGI your quickest path would be to get hold of a C/C++ library and make an XS wrapper for it.

Here is a useful site with source code. The main library it uses lives here and is available for Win32 or Linux with versions that compile with VCC, BCC, and GCC. You will find a wealth of info on Google searching for strings like 'image compare similarity C++'

Thanks for the link, I think Merlyn's column referred to below should work out as a good place to start, but I will keep that in my back pocket. The server it will wind up running on might be Linux, BSD, or Win2k, depending, and I might need to delegate it to someone else, so I really wanted to avoid C/C++ since I wanted to make sure the code was as portable as possible, and setup was as painless as possible. Again, thanks.

I did a cursory search of Merlyn's articles, but somehow I missed the perfect one. Many thanks for that link. That looks like an excellent place to start.

I don't actually think it's very similar. merlyn was trying to find similar images (differing by resolution, format, etc, but otherwise more or less identical) whereas moof1138 is trying to find similar objects - the images he will be comparing will all be lit differently and be photographed against different backgrounds. Removing the backgrounds and adjusting colors based on lighting are both nontrivial tasks. I think the C/C++ based tools mentioned by tachyon in the first post will be your best bet.

This is definitely a cool problem, here's a possible snag that you may run in to:

"My selected strategy is to reduce each image to a 4-by-4 grid of RGB values, yielding a 48-number vector of values from 0 to 255."

Assuming that the counterfeits would at least have similar color schemes to the originals, this has a very good chance of generating a lot of falsely positive matches. You could get around this by increasing the size of the grid (and therefore the length of your vectors), but this is the kind of algorithm in which C/C++ would probably outperform Perl (although you could check out the pdl). Even after making your vectors then you're left with the task of determining a metric for measuring similarity of vectors and deciding how similar two vectors must be in order to assume that they point to the same type of coin.

Here's what I think that I would do:

- Play with generating vectors using the method from Merlyn's article until you find an acceptable balance of speed and sensitivity.
- Use a measure of similarity between vectors (such as Euclidean distance) and compare different images of the same coin.
- Attempt to determine a reasonable threshold for assuming that they point to a counterfeit copy of the same type of coin.


Actually, more realistically I'd put this off until after vacation, when I could go back and ask an imaging scientist friend about the problem. :)

Good luck, and have fun.

When I want to deal with Perl and images, the first place I always start is Image Magick. I am not an image expert so doing this type of work while interesting and valuable is out of my league.

Make sure to also google for "tail-recognition" so you can recognize both sides of the coins ;P

foreach pixel {
     (pixel - min)*255/max
}
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I think comparing edges only may be the key (excellent link, JoshuaD). The edges should capture the features of the coins being compared. Simply comparing images will not work - here's why.
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I guess the hard part would be aligning the images. But maybe you could do a series of resize and rotations and check for minimum difference?

From perldoc Imager::Filters

 Image Difference

  You can create a new image that is the difference 
  between 2 other images.
      my $diff = $img->difference(other=>$other_img);
  For each pixel in $img that is different to the pixel in
  $other_img, the pixel from $other_img is given, otherwise
  the pixel is transparent black.

       This can be used for debugging image differences  ("Where are t
+hey different?"), and for optimizing animated   GIFs.

       Note that $img and $other_img must have the same               
+ number of channels.  The width and heigh of $diff will be the minimu
+m of each of the width and height of $img and
       $other_img.
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