Dynamic estimation in computational vision
Toshio Michael Chin · 1992
Spatial coherence constraints are commonly used to regularize the problems of reconstructing dense visual fields like depth, shape, and motion. Recent developments in theory and practice show that the local nature of spatial coherence constraints allows us to solve single-frame reconstruction problems efficiently with, for example, multiresolution approaches. While it is reasonable to impose temporal as well as spatial coherence on the unknown for a more robust estimation through data fusion over both space and time, such temporal, multi-frame extensions of the problems have not been as widely considered, perhaps due to the different and severe computational demands imposed by the sequential arrival of the image data. We present here an efficient filtering algorithm for sequential estimation of dense visual fields, using stochastic descriptor dynamic system models to capture temporal smoothness and dynamics of the fields. Theoretically, standard Kalman filtering techniques (generalized...