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he first thing to do here is to analyze why the different failure cases are happening. E.g., isolate examples of places where bottles win, where the images are fuzzy, etc., and perform some statistical analysis to learn the difference between their Hough representations and the ones you wish it would detect. Some great places to learn about alternative approaches are here and here 
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@linker Wouldn't be extracting SIFT or SURF features much faster than the hough-transformation ? Why only detect cans when you could detect more registered objects? 
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@stacker makes a good point. For speed you want to get cheap-to-compute features, like histograms of oriented gradients. A really naive first approach would be to manually label a bunch of can rectangles in some training images, and use these plus random negative examples to train an SVM or decision-tree classifier. The training will take longer, but the execution on novel images will be much faster. I'm planning to write this method up when I get more free time to include the right references.   EMS Apr 16 '12 at 4:52
 I did this because the scope of the assignment was specifically aimed at CocaCola cans. I don't know much about SIFT or SURF, but if this algorithm fits this problem I'd love to see a reply on the topic. How about an approach simild be an idea, but you have absolutely no guarantee that the Coca-Cola letters will be visible, they could be totally hidden, partially hidden, or the can could be turned around. And doesn't solve the problem with the bottle either since the letters are the same.  Have You tried to detect the top or bottom seal of the can? It could be possible to detect it as an edge parallel to the edge of the red area.   maniek Apr 16 '12 at I didn't mean for character recognition, I meant using the idea of using humans to do the finding. I.d. show then 2 pictures, one known and one not known. Was jus2	  	
It's a good time to commit to Computer Vision onCan you add few more test images to get a much more idea?   
Did you tried it without converting RGB to HSV? I think your problem of bottles is in your conversion, actually you removed your cans in your first st No the conversion to HSV works fine, it's just so I can eliminate some of the stuff that is clearly not red-like. Can and bottle are still there even after passing to HSV, the problem is mainly how to differentiate between the 2 since the have common characteristics.But seems your sample is saying something else, I think in your first sample after preprocessing images of can removed? would you arrange your samples step by step? i Oh i see what you mean, images 2 and 3 in my question are not of the same original ! In image 3, there was only a can. I could have posted more, but trying to keep the question to a strict minimum. Just assume that the HSV translation is correctly keeping both can and bottles (+ some noise all over the image) I have a great attraction for this kind of software. Anyone knows if there are some mature and well es
 tablished Java Library to do these image recognition?  r openCV just very recently released their java bindings for their library (as of 2.4.4). So, basically you could use openCV in java (without all the fuss of doing JNI manually). I tried it and it worked fine (but it's still buggy since its very recen