We present a formulation for recursive recovery of motion, pointwise structure, and focal length from feature correspondences tracked through an image sequence. In addition to adding focal length to the state vector, several representational improvements are made over earlier structure from motion formulations, yielding a stable and accurate estimation framework which applies uniformly to both ...

This paper considers the Linear Minimum Variance recursive state estimation for the linear discrete time dynamic system with random state transition and measurement matrices, i.e., random parameter matrices Kalman filtering. It is shown that such system can be converted to a linear dynamic system with deterministic parameter matrices but state-dependent process and measurement noises. It is pro...

Deconvolution problems are encountered in signal processing applications where an unknown input signal can only be observed after propagation through one or more noise corrupted FIR channels. The first step in recovering the input usually entails an estimation of the FIR channels through training based or blind algorithms. The ’standard’ procedure then uses least squares estimation to recover t...

Bai (2009) proposes recursive estimation for panel data models with interactive effects. We study the behaviours of this estimator. The formula is established that shows estimators depend on initial estimator, population structure and iterative steps. Under some general scenarios, we find estimator becomes consistent after first iteration from any initials. also obtain optimal number steps unde...