I have a background in classic image processing and machine vision and back in the olden days we had the opposite problem: algorithms were just too specific to build useful applications. It's easy to detect lines and circles with a Hough Transform or do template matching for features that very closely match a sample. However, working up the chain it never came together, detecting cars in a parking lot, a relatively simple task with CNNs, was a very difficult problem.

I wonder if enough work is being done to combine the achievements of each field. Whenever I see adversarial examples I wonder why people aren't doing more preprocessing to root out obvious problems with normalization in scale, color, perspective, etc. Also, if we could feed networks with higher level descriptors instead of feeding low-information-density color images, wouldn't that make life easier.

I'm sure I'm not the only one thinking this, is there any good research being done in that space?