8 April 1993 Square-root filtering in time-sequential estimation of random fields
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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.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Toshio Mike Chin, Toshio Mike Chin, William Clement Karl, William Clement Karl, Arthur J. Mariano, Arthur J. Mariano, Alan S. Willsky, Alan S. Willsky, "Square-root filtering in time-sequential estimation of random fields", Proc. SPIE 1903, Image and Video Processing, (8 April 1993); doi: 10.1117/12.143140; https://doi.org/10.1117/12.143140


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