It is an interesting concept, because it looks like an anti-classifier / anti-segmenter.

Usually we want to maximize identifcation and or segmentation within an image, but now you would want to reverse the cost function in a sense, so as to minimize identifiability. The theoretical best rate that this can occur would be probably be uniform random sampling across a grid.

What you could do is have a set of images for various locations under different conditions / weather, then superimpose the camo in various orientations, and find the which camo performs best in which settings more often.

This would be the quick and dirty start approach, then you can focus in on particular use case / conditions such as the other poseter has commented on.

> varying vegetaion (sage, nothing, large deciduous trees, pines, ...). The person may be laying down in the bushes, walking down an open path, ...