I am doing my first unguided project and just need a little advice. I’ve done a lot of reading and some online courses, but have not been able to find information on how to handle blank space. I do not want to tell exactly what I am doing, but here is some information that I can give:
-I have hundreds of images that are inconsistent shapes (curved edges, different sizes) and cropping part of the image is not an option. I put these images in a rectangular matrix so all my data is the same shape, but because of that I have a bunch of blank pixels.
-There is only 1 feature/class I am trying to model, but there is a large class imbalance in terms of the number of pixels(<1:100), so I am using a binaryfocalloss function. The range of values in the actual images is about 100-400. So I could set the blank pixels to zero then normalize or 100 then normalize. I am not sure it matters.
-For the training data, the features are determined by taking the difference in the values of 2 of the image channels and manually identifying the pattern of that feature.
Bonus question: If my channels have different value ranges (like 100-150 vs 300-400) should I normalize each channel by the range of that channel or the range of all the channels? I think the latter?
Thanks for your help!