I have done quite a bit of work with neural networks (research at university level). I have also written a quick program in C using the FANN library that allows me to click on a bunch of related objects, take a screen shot of that image, and then cascade train a neural network to identify that object. I then can click on an arbitrary object and it will spit out a number between -1 and +1 with +1 being 100% identification.
The speed of a neural network after it has been trained goes like ~N for simple forward prop networks. This is very fast compared to many algorithms which have to "deform" some template and then search repeatedly.
I have it working quite well on find ores in RS. To simplify things I first get a TPA of similar colored objects and scan over these points with the NN. ~99% correct identification at this point. I bet with a little tinkering this could be used to identify more complicated objects/randoms/etc.
Maybe this would be a pointless addition? Let me know your ideas.


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