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

Question

One of the most interesting projects I\'ve worked on in the past couple of years was a project about image processing. The goal was to develop a system to be able to recogni

Answer 1

The first things I would look for are color - like RED , when doing Red eye detection in an image - there is a certain color range to detect , some characteristics about it considering the surrounding area and such as distance apart from the other eye if it is indeed visible in the image.

1: First characteristic is color and Red is very dominant. After detecting the Coca Cola Red there are several items of interest 1A: How big is this red area (is it of sufficient quantity to make a determination of a true can or not - 10 pixels is probably not enough), 1B: Does it contain the color of the Label - "Coca-Cola" or wave. 1B1: Is there enough to consider a high probability that it is a label.

Item 1 is kind of a short cut - pre-process if that doe snot exist in the image - move on.

So if that is the case I can then utilize that segment of my image and start looking more zoom out of the area in question a little bit - basically look at the surrounding region / edges...

2: Given the above image area ID'd in 1 - verify the surrounding points [edges] of the item in question. A: Is there what appears to be a can top or bottom - silver? B: A bottle might appear transparent , but so might a glass table - so is there a glass table/shelf or a transparent area - if so there are multiple possible out comes. A Bottle MIGHT have a red cap, it might not, but it should have either the shape of the bottle top / thread screws, or a cap. C: Even if this fails A and B it still can be a can - partial.. This is more complex when it is partial because a partial bottle / partial can might look the same , so some more processing of measurement of the Red region edge to edge.. small bottle might be similar in size ..

3: After the above analysis that is when I would look at the lettering and the wave logo - because I can orient my search for some of the letters in the words As you might not have all of the text due to not having all of the can, the wave would align at certain points to the text (distance wise) so I could search for that probability and know which letters should exist at that point of the wave at distance x.