Aspects of dynamic programming in signal and image processing

The techniques peculiar to dynamic programming have found a variety of successful applications in the theory and practice of modern control. Successes in the theory and practice of signal and image processing are less numerous and prominent, but they do exist. In this paper, we sound a call for renewed attention to the potential of dynamic programming for solving knotty, nonlinear filtering problems in signal and image processing, and outline successes we have recently enjoyed in nonlinear frequency tracking and random boundary estimation in noisy black and white images. Two classical results, the fast Fourier transform and Levinson's recursion for determining autoregressive parameters, are treated in the context of dynamic programming simply to reinforce the point that many of the algorithms we take for granted, and which were derived without recourse to dynamic programming, can be nicely interpreted as dynamic programming algorithms.