What is the significance of the following equation in computer vision? Say I have a set of sample measurements of a one dimensional curve in the image plane $f(x)$. What is the purpose of minimizing the following functional
$$E(S) = \int \left[\lambda(S''(x))^2 + (f(x) - S(x))^2 \sum_k \delta (x - x_k)\right]dx$$

I believe it has something to do with energy minimization and snakes.
I would agree - energy minimization for snakes (active contours).

It minimizes second derivative - bending of the curve.

Minimizes the difference between the image (probably it's edges) and contour (at discreet contour points - the reason for having Dirac impulse in the expression).

Usually one could expect also to have minimization of the first derivative - stretching of the snake.

More details and references on wiki - active contour