Optimal dynamic tomography for wide-sense stationary spatial random fields

Mark D. Butala, Farzad Kamalabadi

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

Abstract

Dynamic tomography is concerned with the image formation of a temporally changing object from its line integral projections. The problem remains challenging because of its high dimensionality. In this paper, we identify a sufficient class of dynamic tomography problems that can be solved by a state estimator that requires only linear shift-invariant filtering operations. This class includes rigid-body motion, common in biomedical imaging scenarios. The new state estimator is far less computationally demanding than classic methods such as the Kalman filter. Whereas the Kalman filter requires O(N2) memory storage and O(N3) processing for anN-dimensional problem, the state estimator derived in this work requires only O(N) storage and O(N log N) processing.

Original languageEnglish (US)
Title of host publication2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings
PublisherIEEE Computer Society
Pages593-596
Number of pages4
ISBN (Print)9781424456543
DOIs
StatePublished - Jan 1 2009
Event2009 IEEE International Conference on Image Processing, ICIP 2009 - Cairo, Egypt
Duration: Nov 7 2009Nov 10 2009

Publication series

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

Other

Other2009 IEEE International Conference on Image Processing, ICIP 2009
CountryEgypt
CityCairo
Period11/7/0911/10/09

Keywords

  • Kalman filtering
  • Multidimensional signal processing
  • Recursive estimation
  • Remote sensing

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Butala, M. D., & Kamalabadi, F. (2009). Optimal dynamic tomography for wide-sense stationary spatial random fields. In 2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings (pp. 593-596). [5413864] (Proceedings - International Conference on Image Processing, ICIP). IEEE Computer Society. https://doi.org/10.1109/ICIP.2009.5413864