In CV, the first-ish level of processing determines the points on the image that will be used as "individual measurable properties" (or, maybe more accurately, the sources for those). These are the features, and then they go into the rest of the system for recognition, etc.
The basic theme is stripping something analogous to "noise" and "bias" - noise being the stuff you don't want to pay attention to, and "bias" being the predictable/regular offset per thing - because you want to recognize that two things are actually the same thing, despite differences in noise and bias.
(An example of "bias" might be the color of the lights where the picture was taken).
Then the idea is pretty much that, if the same thing is seen in two different places, after stripping the noise and the bias from it, they "look" identical (diff is within an epsilon).
That sound about right?
Feature engineering is the work process where you massage, select, transform, normalize, regularize, standardize, adjust, whiten your input into format that is easy to digest into next phase.
To confuse things even more, another name for feature learning is representation learning. Deep does just this. One layer learns new features that are inputs for next layer.
Real world example I'm working with:
I have raw EEG data from multiple channels and I want to use machine learning to classify it. The raw input is rows of integer values. That represent EEG reading 1 per channel. There are 300 readings per second.
Consider all these issues: 1) every person and every electrode has different level of impedance and noise every time they are attached, 2) during a measurement the impedance changes and noise level changes. 3) there are several periods where person moves messing with the measures. These measurements should be removed automatically.
The pipeline from raw data to the beginning of deep learning pipeline may be as follows: linear and constant detrending, normalize to standard deviation, remove 50 Hz noise, whiten, STFT into 0.5 second frequency segments, (remove epochs where patient moves) run trough carefully adjusted logit function.