c++ - Image Processing: Algorithm Improvement for 'Coca-Cola Can' Recognition

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Top 5 Answer for c++ - Image Processing: Algorithm Improvement for 'Coca-Cola Can' Recognition

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An alternative approach would be to extract features (keypoints) using the scale-invariant feature transform (SIFT) or Speeded Up Robust Features (SURF).

You can find a nice OpenCV code example in Java, C++, and Python on this page: Features2D + Homography to find a known object

Both algorithms are invariant to scaling and rotation. Since they work with features, you can also handle occlusion (as long as enough keypoints are visible).

Enter image description here

Image source: tutorial example

The processing takes a few hundred ms for SIFT, SURF is bit faster, but it not suitable for real-time applications. ORB uses FAST which is weaker regarding rotation invariance.

The original papers

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To speed things up, I would take advantage of the fact that you are not asked to find an arbitrary image/object, but specifically one with the Coca-Cola logo. This is significant because this logo is very distinctive, and it should have a characteristic, scale-invariant signature in the frequency domain, particularly in the red channel of RGB. That is to say, the alternating pattern of red-to-white-to-red encountered by a horizontal scan line (trained on a horizontally aligned logo) will have a distinctive "rhythm" as it passes through the central axis of the logo. That rhythm will "speed up" or "slow down" at different scales and orientations, but will remain proportionally equivalent. You could identify/define a few dozen such scanlines, both horizontally and vertically through the logo and several more diagonally, in a starburst pattern. Call these the "signature scan lines."

Signature scan line

Searching for this signature in the target image is a simple matter of scanning the image in horizontal strips. Look for a high-frequency in the red-channel (indicating moving from a red region to a white one), and once found, see if it is followed by one of the frequency rhythms identified in the training session. Once a match is found, you will instantly know the scan-line's orientation and location in the logo (if you keep track of those things during training), so identifying the boundaries of the logo from there is trivial.

I would be surprised if this weren't a linearly-efficient algorithm, or nearly so. It obviously doesn't address your can-bottle discrimination, but at least you'll have your logos.

(Update: for bottle recognition I would look for coke (the brown liquid) adjacent to the logo -- that is, inside the bottle. Or, in the case of an empty bottle, I would look for a cap which will always have the same basic shape, size, and distance from the logo and will typically be all white or red. Search for a solid color eliptical shape where a cap should be, relative to the logo. Not foolproof of course, but your goal here should be to find the easy ones fast.)

(It's been a few years since my image processing days, so I kept this suggestion high-level and conceptual. I think it might slightly approximate how a human eye might operate -- or at least how my brain does!)

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Fun problem: when I glanced at your bottle image I thought it was a can too. But, as a human, what I did to tell the difference is that I then noticed it was also a bottle...

So, to tell cans and bottles apart, how about simply scanning for bottles first? If you find one, mask out the label before looking for cans.

Not too hard to implement if you're already doing cans. The real downside is it doubles your processing time. (But thinking ahead to real-world applications, you're going to end up wanting to do bottles anyway ;-)

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Isn't it difficult even for humans to distinguish between a bottle and a can in the second image (provided the transparent region of the bottle is hidden)?

They are almost the same except for a very small region (that is, width at the top of the can is a little small while the wrapper of the bottle is the same width throughout, but a minor change right?)

The first thing that came to my mind was to check for the red top of bottle. But it is still a problem, if there is no top for the bottle, or if it is partially hidden (as mentioned above).

The second thing I thought was about the transparency of bottle. OpenCV has some works on finding transparent objects in an image. Check the below links.

Particularly look at this to see how accurately they detect glass:

See their implementation result:

Enter image description here

They say it is the implementation of the paper "A Geodesic Active Contour Framework for Finding Glass" by K. McHenry and J. Ponce, CVPR 2006.

It might be helpful in your case a little bit, but problem arises again if the bottle is filled.

So I think here, you can search for the transparent body of the bottles first or for a red region connected to two transparent objects laterally which is obviously the bottle. (When working ideally, an image as follows.)

Enter image description here

Now you can remove the yellow region, that is, the label of the bottle and run your algorithm to find the can.

Anyway, this solution also has different problems like in the other solutions.

  1. It works only if your bottle is empty. In that case, you will have to search for the red region between the two black colors (if the Coca Cola liquid is black).
  2. Another problem if transparent part is covered.

But anyway, if there are none of the above problems in the pictures, this seems be to a better way.

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I really like Darren Cook's and stacker's answers to this problem. I was in the midst of throwing my thoughts into a comment on those, but I believe my approach is too answer-shaped to not leave here.

In short summary, you've identified an algorithm to determine that a Coca-Cola logo is present at a particular location in space. You're now trying to determine, for arbitrary orientations and arbitrary scaling factors, a heuristic suitable for distinguishing Coca-Cola cans from other objects, inclusive of: bottles, billboards, advertisements, and Coca-Cola paraphernalia all associated with this iconic logo. You didn't call out many of these additional cases in your problem statement, but I feel they're vital to the success of your algorithm.

The secret here is determining what visual features a can contains or, through the negative space, what features are present for other Coke products that are not present for cans. To that end, the current top answer sketches out a basic approach for selecting "can" if and only if "bottle" is not identified, either by the presence of a bottle cap, liquid, or other similar visual heuristics.

The problem is this breaks down. A bottle could, for example, be empty and lack the presence of a cap, leading to a false positive. Or, it could be a partial bottle with additional features mangled, leading again to false detection. Needless to say, this isn't elegant, nor is it effective for our purposes.

To this end, the most correct selection criteria for cans appear to be the following:

  • Is the shape of the object silhouette, as you sketched out in your question, correct? If so, +1.
  • If we assume the presence of natural or artificial light, do we detect a chrome outline to the bottle that signifies whether this is made of aluminum? If so, +1.
  • Do we determine that the specular properties of the object are correct, relative to our light sources (illustrative video link on light source detection)? If so, +1.
  • Can we determine any other properties about the object that identify it as a can, including, but not limited to, the topological image skew of the logo, the orientation of the object, the juxtaposition of the object (for example, on a planar surface like a table or in the context of other cans), and the presence of a pull tab? If so, for each, +1.

Your classification might then look like the following:

  • For each candidate match, if the presence of a Coca Cola logo was detected, draw a gray border.
  • For each match over +2, draw a red border.

This visually highlights to the user what was detected, emphasizing weak positives that may, correctly, be detected as mangled cans.

The detection of each property carries a very different time and space complexity, and for each approach, a quick pass through http://dsp.stackexchange.com is more than reasonable for determining the most correct and most efficient algorithm for your purposes. My intent here is, purely and simply, to emphasize that detecting if something is a can by invalidating a small portion of the candidate detection space isn't the most robust or effective solution to this problem, and ideally, you should take the appropriate actions accordingly.

And hey, congrats on the Hacker News posting! On the whole, this is a pretty terrific question worthy of the publicity it received. :)

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