### Abstract

As a time-sequential and Bayesian front-end for image sequence processing, we consider the square root information (SRI) realization of Kalman filter. The computational complexity of the filter due to the dimension of the problem - the size of the state vector is on the order of the number of pixels in the image frame - is decreased drastically using a reduced-order approximation exploiting the natural spatial locality in the random field specifications. The actual computation for the reduced-order SRI filter is performed by an iterative and distributed algorithm for the unitary transformation steps, providing a potentially faster alternative to the common QR factorization-based methods. For the space-time estimation problems, near-optimal solutions can be obtained in a small number of iterations (e.g. less than 10), and each iteration can be performed in a finely parallel manner over the image frame, an attractive feature for a dedicated hardware implementation.

Original language | English (US) |
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Pages (from-to) | 51-58 |

Number of pages | 8 |

Journal | Proceedings of SPIE - The International Society for Optical Engineering |

Volume | 1903 |

DOIs | |

State | Published - Apr 8 1993 |

Event | Image and Video Processing 1993 - San Jose, United States Duration: Jan 31 1993 → Feb 5 1993 |

### ASJC Scopus subject areas

- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering

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## Cite this

*Proceedings of SPIE - The International Society for Optical Engineering*,

*1903*, 51-58. https://doi.org/10.1117/12.143140