A particle filter framework using optimal importance function for protein molecules tracking

Q. Wen, J. Gao, A. Kosaka, H. Iwaki, K. Luby-Phelps, D. Mundy

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

5 Scopus citations

Abstract

Tagging and tracking protein molecules are a key to a better understanding of proteomics in diverse aspects. In this paper, a common framework of particle filter using optimal importance function is proposed for confocal protein molecules tracking. To deal with the challenges stemming from small size, deformable shape, noisy environment, and multi-modality motion, a stochastic process based particle filter is used. Partial Gaussian State Space (PGSS) model is developed as the importance function to incorporate the latest measurement in the state estimation. Experimental results have demonstrated the performance of the proposed algorithm for both Brownian and translational motion.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Image Processing 2005, ICIP 2005
Pages1161-1164
Number of pages4
DOIs
StatePublished - Dec 1 2005
EventIEEE International Conference on Image Processing 2005, ICIP 2005 - Genova, Italy
Duration: Sep 11 2005Sep 14 2005

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume1
ISSN (Print)1522-4880

Other

OtherIEEE International Conference on Image Processing 2005, ICIP 2005
CountryItaly
CityGenova
Period9/11/059/14/05

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Wen, Q., Gao, J., Kosaka, A., Iwaki, H., Luby-Phelps, K., & Mundy, D. (2005). A particle filter framework using optimal importance function for protein molecules tracking. In IEEE International Conference on Image Processing 2005, ICIP 2005 (pp. 1161-1164). [1529962] (Proceedings - International Conference on Image Processing, ICIP; Vol. 1). https://doi.org/10.1109/ICIP.2005.1529962