> "so-called 'computer vision' algorithms that are little more than simple image processing"
Implementing any learning algorithm should be the absolute last resort. They're complicated, hard to generalize, and difficult to guarantee in the real world. Not that image processing is much better, but it's definitely more deterministic and predictable in its results. With any system geared toward a specific goal, your first approach should be to look at the simplest solutions (no matter how rudimentary they may be).
> It is tempting to dismiss sophisticated techniques because (a) they are hard to understand and (b) the task seems so easy to our own brain-equipped vision systems. But the fact is, most interesting computer vision problems (including this one) require sophisticated representations to achieve robustness and generality. In other words, any good solution will need an answer to the question "What is a Waldo?" that is better than "a 50x50 patch of pixels with red and white stripes".
Yes, of course. I'm using waldo as an example toward a problem I often see reading research papers, that is an overly complex system made to try to solve more than just the initial task. "What is a Waldo" is irrelevant to the task at hand. Identifying the best candidate for Waldo is the goal. In this situation simple correlation (or even more efficient correlation methods: MMCF, OTSDF, QCF, etc) can give good results (except on that all waldos page...damn that's even hard for a human) most of the time.
To make a euchre analogy - you don't pass on a biddable hand, and you don't waste your trump cards.