Using a Connected Filter for Structure Estimation in Perspective Systems

Three-dimensional structure information can be estimated from two-dimensional images using recursive estimation methods. This paper investigates possibilities to improve structure filter performance for a certain class of stochastic perspective systems by utilizing mutual information, in particular when each observed point on a rigid object is affected by the same process noise. After presenting the dynamic system of interest, the method is applied, using an extended Kalman filter for the estimation, to a simulated time-varying multiple point vision system. The performance of a connected filter is compared, using Monte Carlo methods, to that of a set of independent filters. The idea is then further illustrated and analyzed by means of a simple linear system. Finally more formal stochastic differential equation aspects, especially the impact of transformations in the Itô sense, are discussed and related to physically realistic noise models in vision systems.