Joint multi-object and clutter rate estimation with the single-cluster PHD filter

Isabel Schlangen; Vibhav Bharti; Emmanuel Delande; Daniel E. Clark

doi:10.1109/ISBI.2017.7950705

Joint multi-object and clutter rate estimation with the single-cluster PHD filter

Isabel Schlangen, Vibhav Bharti, Emmanuel Delande, Daniel E. Clark

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

7 Citations (Scopus)

Abstract

When working with real data, underlying parameters such as the detection or clutter rates are generally unknown and possibly varying over time, however the right parametrisation is crucial to extract proper statistics about the monitored objects. In this article, a single cluster Probability Hypothesis Density (PHD) filter is used to jointly estimate the location and number of a set of objects and the clutter rate over time. The algorithm is verified on a simulated scenario designed to emulate the challenging nature of Single-Molecule Localisation Microscopy (SMLM) imaging sequences and demonstrated on a similar scenario with real data.

Original language	English
Title of host publication	2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
Publisher	IEEE
Pages	1087-1091
Number of pages	5
ISBN (Electronic)	9781509011711
DOIs	https://doi.org/10.1109/ISBI.2017.7950705
Publication status	Published - 19 Jun 2017
Event	14th IEEE International Symposium on Biomedical Imaging 2017 - Melbourne, Australia Duration: 18 Apr 2017 → 21 Apr 2017

Conference

Conference	14th IEEE International Symposium on Biomedical Imaging 2017
Abbreviated title	ISBI 2017
Country/Territory	Australia
City	Melbourne
Period	18/04/17 → 21/04/17

Keywords

Clutter estimation
Multi-target tracking
Single-cluster PHD filter
Single-molecule localisation microscopy

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/ISBI.2017.7950705

Cite this

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title = "Joint multi-object and clutter rate estimation with the single-cluster PHD filter",

abstract = "When working with real data, underlying parameters such as the detection or clutter rates are generally unknown and possibly varying over time, however the right parametrisation is crucial to extract proper statistics about the monitored objects. In this article, a single cluster Probability Hypothesis Density (PHD) filter is used to jointly estimate the location and number of a set of objects and the clutter rate over time. The algorithm is verified on a simulated scenario designed to emulate the challenging nature of Single-Molecule Localisation Microscopy (SMLM) imaging sequences and demonstrated on a similar scenario with real data.",

keywords = "Clutter estimation, Multi-target tracking, Single-cluster PHD filter, Single-molecule localisation microscopy",

author = "Isabel Schlangen and Vibhav Bharti and Emmanuel Delande and Clark, {Daniel E.}",

year = "2017",

month = jun,

day = "19",

doi = "10.1109/ISBI.2017.7950705",

language = "English",

pages = "1087--1091",

booktitle = "2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017",

publisher = "IEEE",

address = "United States",

note = "14th IEEE International Symposium on Biomedical Imaging 2017, ISBI 2017 ; Conference date: 18-04-2017 Through 21-04-2017",

}

Schlangen, I, Bharti, V, Delande, E & Clark, DE 2017, Joint multi-object and clutter rate estimation with the single-cluster PHD filter. in 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017., 7950705, IEEE, pp. 1087-1091, 14th IEEE International Symposium on Biomedical Imaging 2017, Melbourne, Australia, 18/04/17. https://doi.org/10.1109/ISBI.2017.7950705

TY - GEN

T1 - Joint multi-object and clutter rate estimation with the single-cluster PHD filter

AU - Schlangen, Isabel

AU - Bharti, Vibhav

AU - Delande, Emmanuel

AU - Clark, Daniel E.

PY - 2017/6/19

Y1 - 2017/6/19

N2 - When working with real data, underlying parameters such as the detection or clutter rates are generally unknown and possibly varying over time, however the right parametrisation is crucial to extract proper statistics about the monitored objects. In this article, a single cluster Probability Hypothesis Density (PHD) filter is used to jointly estimate the location and number of a set of objects and the clutter rate over time. The algorithm is verified on a simulated scenario designed to emulate the challenging nature of Single-Molecule Localisation Microscopy (SMLM) imaging sequences and demonstrated on a similar scenario with real data.

AB - When working with real data, underlying parameters such as the detection or clutter rates are generally unknown and possibly varying over time, however the right parametrisation is crucial to extract proper statistics about the monitored objects. In this article, a single cluster Probability Hypothesis Density (PHD) filter is used to jointly estimate the location and number of a set of objects and the clutter rate over time. The algorithm is verified on a simulated scenario designed to emulate the challenging nature of Single-Molecule Localisation Microscopy (SMLM) imaging sequences and demonstrated on a similar scenario with real data.

KW - Clutter estimation

KW - Multi-target tracking

KW - Single-cluster PHD filter

KW - Single-molecule localisation microscopy

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U2 - 10.1109/ISBI.2017.7950705

DO - 10.1109/ISBI.2017.7950705

M3 - Conference contribution

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EP - 1091

BT - 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017

PB - IEEE

T2 - 14th IEEE International Symposium on Biomedical Imaging 2017

Y2 - 18 April 2017 through 21 April 2017

ER -

Joint multi-object and clutter rate estimation with the single-cluster PHD filter

Abstract

Conference

Keywords

ASJC Scopus subject areas

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