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, ...

How would this work in practice? You pull up to the location, pan a digital camera around the area, the system makes a recommendation and you strip down and change into the selected camouflage?

I think something like this could be done, but I would think that most people would be able to make this judgment upon seeing the physical environment.

Some technical challenges which come to mind:

Any location will likely have varying lighting conditions (in bright sunlight out in the open, in semi-shaded areas, in large shadows from trees and large rocks, backlighting, ...), varying vegetaion (sage, nothing, large deciduous trees, pines, ...). The person may be laying down in the bushes, walking down an open path, ...

I’m very into the outdoors too and was wondering about this very thing not long ago when I was trying to decide between ConCamo or PenCott Greenzone for my area.

I don’t know if you would need an ML-based solution to this. One way to frame the problem is that you would be trying to find a pattern that has the closest colour channels to the environment. I can’t remember my exact thinking now, but I think there may be simpler ways to assess this, and I’m not sure that there’s any “learning” required of the system. That said, a way to measure how good a match is could be to try and maximise the number of false negatives of a simple object detection network like Faster-RCNN when shown a given camo against multiple photos of the area intended for use.

I mean it's a very good project to develop but a visual inspection can solve it? Unless you perform an automation process of generating camo for something to be placed in the wild like a box for a camera or something.

But in fact is a very good ML project