In this paper we show how the theory of dynamical systems can be employed to solve problems in motion vision. In particular we develop algorithms for the recovery of dense depth maps and motion parameters using state space observers or filters. Four different dynamical models of the imaging situation are investigated and corresponding filters/ observers derived. The most powerful of these algorithms recovers depth and motion of general nature using a brightness change constraint assumption. No feature-matching preprocessor is required.