Reducing "Structure From Motion": A General Framework for Dynamic Vision Part 2: Implementation and Experimental Assessment
Issue No. 09 - September (1998 vol. 20)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.713361
<p><b>Abstract</b>—A number of methods have been proposed in the literature for estimating scene-structure and ego-motion from a sequence of images using dynamical models. Despite the fact that all methods may be derived from a "natural" dynamical model within a unified framework, from an engineering perspective there are a number of trade-offs that lead to different strategies depending upon the applications and the goals one is targeting. We want to characterize and compare the properties of each model such that the engineer may choose the one best suited to the specific application. We analyze the properties of filters derived from each dynamical model under a variety of experimental conditions, assess the accuracy of the estimates, their robustness to measurement noise, sensitivity to initial conditions and visual angle, effects of the bas-relief ambiguity and occlusions, dependence upon the number of image measurements and their sampling rate.</p>
Computer vision, structure from motion (SFM), shape estimation, recursive filter, nonlinear implicit extended Kalman filter.
S. Soatto and P. Perona, "Reducing "Structure From Motion": A General Framework for Dynamic Vision Part 2: Implementation and Experimental Assessment," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 20, no. , pp. 943-960, 1998.