1) Start working on the final code. Figure out what I need, if I'm going to do it on Windows/Linux, what libaries I will need, ect. I believe that I am in a good enough position for detecting an X from a non X, that it is time to move on from that sole problem. I can do somethings with real time data when detecting an X (like an average if there is an X over multiple frames), that is not possible when just detecting an X in a single image.
2) Collect data for Os and finish labeling data for Xs.
3) Out of the 3000 random pixel comparisions I extract and pass in for detecing a X compared to a non X, in the current ADT only 31 are used. Wut.
So what this means is that I can use 10,000, or mabye even 100,000 random comparisions, have jboost crunch numbers over night, and then just tell me what are the most useful comparisions. I can then extract what are the useful comparisions from the random values and then only pass in those and have extremely fast, extremely accurate code. When checking for an X there will be no need to extract all 3000 comparision, pass them all in, and then ONLY have 31 be used.
With the current 3000 random pixel comparisions the results are pretty good, but there are probably some comparisions that are better. By using 10,000 or 100,000 comparisions these better comparisions will be picked out and give even better results.
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