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

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

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

11 Citations (Scopus)

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

Access to Document

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 - http://www.scopus.com/inward/record.url?scp=84973369673&partnerID=8YFLogxK

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

Access to Document

Other files and links

Fingerprint

Cite this