That also demonstrates the difference between having the possibility to optimize input until the output matches the known values and having to process "real life" images made under different conditions.
The later is certainly more interesting: e.g. I dream of having an OCR that is "always right" no matter how bad the image is.
Controlled lighting is extremely helpful in computer vision and key to the operation of a lot of industrial uses of it. From a system design point of view it makes a lot of sense, but obviously it's not possible in many applications (and researchers obviously want to go after the toughest problems, and would rather mostly solve a hard problem than entirely solve an easier one).
What would it take to determine which brand (skittles, m&ms etc) and which sub-brand (tropical / peanut, etc) and how many calories are on the table?
In my head, you would determine the relative color difference of each piece to determine the brand, but I'm not 100% clear on how that kind of ai works.
Thanks. I have no idea if it's the "right" way to do this. It's the first computer vision problem I've done in a long time.
With better lighting you could probably distinguish between colors of different types of candy, assuming they're different enough. If you can determine the type of candy and know how many calories are in each piece it would be easy to count the calories, of course.
FYI all of your new comments are being marked as [dead] for some reason.
