Target detection for depth imaging using sparse single-photon data

Yoann Altmann; Ximing Ren; Aongus McCarthy; Gerald S. Buller; Steve McLaughlin

doi:10.1109/ICASSP.2016.7472279

Target detection for depth imaging using sparse single-photon data

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

Abstract

This paper presents a new Bayesian model and associated algorithm for depth and intensity profiling using full waveforms from time-correlated single-photon counting (TCSPC) measurements when the photon count in very low. The model represents each Lidar waveform as an unknown constant background level, which is combined in the presence of a target, to a known impulse response weighted by the target intensity and finally corrupted by Poisson noise. The joint target detection and depth imaging problem is expressed as a pixel-wise model selection problem which is solved using Bayesian inference. A Reversible Jump Markov chain Monte Carlo algorithm is proposed to compute the Bayesian estimates of interest. Finally, the benefits of the methodology are demonstrated through a series of experiments using real data.

Original language	English
Title of host publication	2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publisher	IEEE
Pages	3256-3260
Number of pages	5
ISBN (Print)	9781479999880
DOIs	https://doi.org/10.1109/ICASSP.2016.7472279
Publication status	Published - 2016
Event	41st IEEE International Conference on Acoustics, Speech and Signal Processing 2016 - Shanghai International Convention Center, Shanghai, China Duration: 20 Mar 2016 → 25 Mar 2016

Conference

Conference	41st IEEE International Conference on Acoustics, Speech and Signal Processing 2016
Abbreviated title	ICASSP 2016
Country/Territory	China
City	Shanghai
Period	20/03/16 → 25/03/16

Keywords

Bayesian estimation
depth imaging
Full waveform Lidar
Poisson statistics
Reversible Jump Markov Chain Monte Carlo

ASJC Scopus subject areas

Signal Processing
Software
Electrical and Electronic Engineering

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10.1109/ICASSP.2016.7472279

Cite this

@inproceedings{79d46bd3eb514a06a5e63bd1e7450502,

title = "Target detection for depth imaging using sparse single-photon data",

abstract = "This paper presents a new Bayesian model and associated algorithm for depth and intensity profiling using full waveforms from time-correlated single-photon counting (TCSPC) measurements when the photon count in very low. The model represents each Lidar waveform as an unknown constant background level, which is combined in the presence of a target, to a known impulse response weighted by the target intensity and finally corrupted by Poisson noise. The joint target detection and depth imaging problem is expressed as a pixel-wise model selection problem which is solved using Bayesian inference. A Reversible Jump Markov chain Monte Carlo algorithm is proposed to compute the Bayesian estimates of interest. Finally, the benefits of the methodology are demonstrated through a series of experiments using real data.",

keywords = "Bayesian estimation, depth imaging, Full waveform Lidar, Poisson statistics, Reversible Jump Markov Chain Monte Carlo",

author = "Yoann Altmann and Ximing Ren and Aongus McCarthy and Buller, \{Gerald S.\} and Steve McLaughlin",

year = "2016",

doi = "10.1109/ICASSP.2016.7472279",

language = "English",

isbn = "9781479999880",

pages = "3256--3260",

booktitle = "2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",

publisher = "IEEE",

address = "United States",

note = "41st IEEE International Conference on Acoustics, Speech and Signal Processing 2016, ICASSP 2016 ; Conference date: 20-03-2016 Through 25-03-2016",

}

Altmann, Y, Ren, X, McCarthy, A , Buller, GS & McLaughlin, S 2016, Target detection for depth imaging using sparse single-photon data. in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 3256-3260, 41st IEEE International Conference on Acoustics, Speech and Signal Processing 2016, Shanghai, China, 20/03/16. https://doi.org/10.1109/ICASSP.2016.7472279

TY - GEN

T1 - Target detection for depth imaging using sparse single-photon data

AU - Altmann, Yoann

AU - Ren, Ximing

AU - McCarthy, Aongus

AU - Buller, Gerald S.

AU - McLaughlin, Steve

PY - 2016

Y1 - 2016

N2 - This paper presents a new Bayesian model and associated algorithm for depth and intensity profiling using full waveforms from time-correlated single-photon counting (TCSPC) measurements when the photon count in very low. The model represents each Lidar waveform as an unknown constant background level, which is combined in the presence of a target, to a known impulse response weighted by the target intensity and finally corrupted by Poisson noise. The joint target detection and depth imaging problem is expressed as a pixel-wise model selection problem which is solved using Bayesian inference. A Reversible Jump Markov chain Monte Carlo algorithm is proposed to compute the Bayesian estimates of interest. Finally, the benefits of the methodology are demonstrated through a series of experiments using real data.

AB - This paper presents a new Bayesian model and associated algorithm for depth and intensity profiling using full waveforms from time-correlated single-photon counting (TCSPC) measurements when the photon count in very low. The model represents each Lidar waveform as an unknown constant background level, which is combined in the presence of a target, to a known impulse response weighted by the target intensity and finally corrupted by Poisson noise. The joint target detection and depth imaging problem is expressed as a pixel-wise model selection problem which is solved using Bayesian inference. A Reversible Jump Markov chain Monte Carlo algorithm is proposed to compute the Bayesian estimates of interest. Finally, the benefits of the methodology are demonstrated through a series of experiments using real data.

KW - Bayesian estimation

KW - depth imaging

KW - Full waveform Lidar

KW - Poisson statistics

KW - Reversible Jump Markov Chain Monte Carlo

UR - https://www.scopus.com/pages/publications/84973369673

U2 - 10.1109/ICASSP.2016.7472279

DO - 10.1109/ICASSP.2016.7472279

M3 - Conference contribution

AN - SCOPUS:84973369673

SN - 9781479999880

SP - 3256

EP - 3260

BT - 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

PB - IEEE

T2 - 41st IEEE International Conference on Acoustics, Speech and Signal Processing 2016

Y2 - 20 March 2016 through 25 March 2016

ER -

Target detection for depth imaging using sparse single-photon data

Abstract

Conference

Keywords

ASJC Scopus subject areas

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