I am struggling with the following problem. Images are captured using different settings. Each images is used to generate a binary mask. All these binary mask are combined using the OR-operator in order to get the final binary mask. Multiple of the input masks might be redundant. In order to speed up the capturing process, I would like to determine the redundant binary masks. The tricky part is that possibly a the partial masks might contain single pixels that is not redundant, but I want to ignore this right at the moment. I have a data set of approximately 200 final masks and all the corresponding partial masks are available.
I tried to visualize the task and I would like to know what would be the best approach to solve this problem. Is is machine learning or is there an other solution?
Thanks in advance for answers and inspirations
Sylvia