Incremental algorithms for depth-from-motion

The Bayesian models which we have developed in this book allow us to obtain optimal estimates of static visible surfaces, to integrate information from multiple viewpoints, and to analyze the uncertainty in our estimates. Many computer vision applications, however, deal with dynamic environments. This may involve tracking moving objects or updating the model of the environment as the observer moves around. Recent results by Aloimonos et al. (1987) suggest that taking an active role in vision (either through eye or observer movements) greatly simplifies the complexity of certain low-level vision problems. In this chapter, we will examine one such problem, namely the recovery of depth from motion sequences.