In the paper [0] there is more info than just what's explained in the simplified blog post. A.I. is needed if you want to do the same without the color chart used for training it. Furthermore, it is not just color correction, also backscatter is taken into account.
Yes, I'm talking about this paper exactly. All I can see is photogrammetry and regular image processing stuff. There's no DNNs or other hot stuff. I guess the part where they're estimating parameters for the equations from photos counts as ML, but people have stopped calling this stuff "AI" quite a while ago.
I think it is more that this correction allows more generic algorithms like object classification to work.
Of course you could do object classification without color, but that would be throwing away information.
It is just that the back scattering currently is overwhelming the colors in such a variable way that it is hard to use color inclusive object classification without doing stuff like requiring the exact same distance between camera and object for each picture. And likely that doesn't even work yet.
> I guess the part where they're estimating parameters for the equations from photos counts as ML, but people have stopped calling this stuff "AI" quite a while ago.
Indeed- as that coined term suggests, "AI" has such a philosophically-tied definition with moving goalposts, that in practice it gets slapped far too liberally on anything 'magical' having to do with computation. (As long as it's still trendy to do so, at least.)
'Machine Learning' is at least a little more specific of a term (if still rather general).
People point at this all the time, but there's also another AI effect. People use traditional techniques to solve modern problems and label it "AI." Calling it a traditional technique despite the modern application doesn't mean it's moving the goalposts.
[0] http://openaccess.thecvf.com/content_CVPR_2019/papers/Akkayn...