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

doi:10.1109/ICIP.2005.1529962

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 proceeding › Conference 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 language	English (US)
Title of host publication	IEEE International Conference on Image Processing 2005, ICIP 2005
Pages	1161-1164
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2005.1529962
State	Published - 2005
Event	IEEE International Conference on Image Processing 2005, ICIP 2005 - Genova, Italy Duration: Sep 11 2005 → Sep 14 2005

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
Volume	1
ISSN (Print)	1522-4880

Other

Other	IEEE International Conference on Image Processing 2005, ICIP 2005
Country/Territory	Italy
City	Genova
Period	9/11/05 → 9/14/05

ASJC Scopus subject areas

General Engineering

Access to Document

10.1109/ICIP.2005.1529962

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). Article 1529962 (Proceedings - International Conference on Image Processing, ICIP; Vol. 1). https://doi.org/10.1109/ICIP.2005.1529962

A particle filter framework using optimal importance function for protein molecules tracking. / Wen, Q.; Gao, J.; Kosaka, A. et al.
IEEE International Conference on Image Processing 2005, ICIP 2005. 2005. p. 1161-1164 1529962 (Proceedings - International Conference on Image Processing, ICIP; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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., 1529962, Proceedings - International Conference on Image Processing, ICIP, vol. 1, pp. 1161-1164, IEEE International Conference on Image Processing 2005, ICIP 2005, Genova, Italy, 9/11/05. https://doi.org/10.1109/ICIP.2005.1529962

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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.",

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

year = "2005",

doi = "10.1109/ICIP.2005.1529962",

language = "English (US)",

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series = "Proceedings - International Conference on Image Processing, ICIP",

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AU - Mundy, D.

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AB - 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.

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

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