Linear Space-Invariant System Identification and Mismatch Bounds for Estimation of Dynamical Images

Helmuth J. Naumer, Farzad Kamalabadi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

For linear space-invariant temporal systems, we provide a lower bound on the penalty incurred by approximating system dynamics in a Kalman filter by a random walk model, a common model when dynamics are unknown. We then present a computationally tractable algorithm for system identification of high-dimensional linear space-invariant dynamical systems, whereby the circulant structure of the state transition operator yields an estimate of the governing dynamics from a small number of temporal steps. By completing all operations in the frequency domain, we efficiently provide an estimate of the system dynamics and the state of the system. The estimation of system dynamics greatly improves the state estimation over the random walk model, suggesting classical estimators may remain applicable in modern imaging tasks.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings
PublisherIEEE Computer Society
Pages2920-2924
Number of pages5
ISBN (Electronic)9781728163956
DOIs
StatePublished - Oct 2020
Event2020 IEEE International Conference on Image Processing, ICIP 2020 - Virtual, Abu Dhabi, United Arab Emirates
Duration: Sep 25 2020Sep 28 2020

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2020-October
ISSN (Print)1522-4880

Conference

Conference2020 IEEE International Conference on Image Processing, ICIP 2020
CountryUnited Arab Emirates
CityVirtual, Abu Dhabi
Period9/25/209/28/20

Keywords

  • Dynamical Systems
  • Model Mismatch
  • Sequential Estimation
  • Space-Invariant
  • System Identification

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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