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 It^o sense, are discussed and related to physically realistic noise models in vision systems.

• Type: Conference
• Year: 2006
• Where: ECCV
• Authors: Fredrik Nyberg, Ola Dahl, Jan Holst, Anders Heyden
• Keywords: Computer Vision, ECCV 2006, Recursive Estimation Methods, Structure Filter Performance, Vision Systems