Efficient acquisition and learning of fluorescence microscope data models

Charles Jackson, Robert F. Murphy, Jelena Kovačević

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

Abstract

We present a method for efficient acquisition of fluorescence microscope datasets, to allow for higher spatial and temporal resolution, and with less damage from photobleaching. Our proposal is to restrict acquisition to regions where we expect to and an object. Given that the objects are continuously moving, we must have an accurate model to describe objects' motion to predict their future locations. We outline a system for learning and applying this motion model, provide details from some simple simulations, and summarize results from more complex applications.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Image Processing, ICIP 2007 Proceedings
PublisherIEEE Computer Society
Pages245-248
Number of pages4
ISBN (Print)1424414377, 9781424414376
DOIs
StatePublished - 2006
Event14th IEEE International Conference on Image Processing, ICIP 2007 - San Antonio, TX, United States
Duration: Sep 16 2007Sep 19 2007

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume6
ISSN (Print)1522-4880

Other

Other14th IEEE International Conference on Image Processing, ICIP 2007
CountryUnited States
CitySan Antonio, TX
Period9/16/079/19/07

Keywords

  • Fluorescence
  • Microscopy
  • Monte Carlo methods
  • State space methods
  • Tracking

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Jackson, C., Murphy, R. F., & Kovačević, J. (2006). Efficient acquisition and learning of fluorescence microscope data models. In 2007 IEEE International Conference on Image Processing, ICIP 2007 Proceedings (pp. 245-248). [4379567] (Proceedings - International Conference on Image Processing, ICIP; Vol. 6). IEEE Computer Society. https://doi.org/10.1109/ICIP.2007.4379567