If the title wan not very clear, I want a method to take an input image like this,
[[0, 0, 0, 0], [1, 1, 1, 0], [1, 1, 1, 0], [0, 1, 1, 0]]
and output the 2D coordinates of the
1s of the image (So that I can recreate the image)
The application is a robot creating the image using some kind of building blocks, placing one block after the other
I want the output to be sequential because I need to reconstruct the input image pixel by pixel and there are some conditions on the image construction order (e.g. You cannot place a
1 somewhere when it is surrounded by
The image can change and the number of
1s in the image too.
- What is an appropriate AI method to apply in this case?
- How should I feed the image to the network? (Will flattening it to 2D affect my need of an output order?)
- Should I get the output coordinates one by one or as an ordered 2xN matrix?
- If one by one, should I feed the same image for each output or the image without the
1s already filled?
I have tried to apply "NeuroEvolution of Augmenting Topologies" for this using neat-python but was unsuccessful. I am currently looking at RNNs but I am not sure if it is the best choice either.