O Data

Over the past week or so I have been retracing all my steps that I used to successfully create 3 ADTrees to really accuratlly determine an X given a series of images (aka a live video stream), except I have been been doing it to determine an O. Overall it has been just as successful, or even more successful than determing the location of an X.

Also, by retracing all my steps it has given me a chance to refine my pipeline, and in the process I have discovered better was to do what I am doing (will explain in the next post).

Example of an O:

Results:
Red, Green, and Blue are related to the red, green, and blue sections from whem I was doing the Xs. They were pulled from the same subset of random numbers.

For Xs, usefulness was Blue >> Green >>>>> Red

For Os, it is something like this. Red >>>>>>>>>>>>> Green > Blue.

Lol random numbers.
Here are some sample result images:

[TODO: Upload whole album]

And just to prove that blue and green exist....

They just never fire. It is at the point were it made me triple check everything to make sure I was not doing anything wrong with the blue and green classifiers, and as far as I can tell I am not. If I lower the threshold for what a positive is, blue and green fire a lot more, but then red fires too much and has a lot of false positives.