Markov chain monte carlo algorithms for 3D ranging and imaging

Sergio Hernandez-Marin; Andrew Michael Wallace; Gavin Jarvis Gibson

Markov chain monte carlo algorithms for 3D ranging and imaging

Sergio Hernandez-Marin^*, Andrew Michael Wallace, Gavin Jarvis Gibson

^*Corresponding author for this work

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

8 Citations (Scopus)

Abstract

We propose a new approach for the processing of Time- Correlated Single Photon Count (TCSPC) and Burst Illumination Laser (BIL) data. This data can be used to measure range, surface shape and determine a characteristic signature for remote targets. In general, the problem is to analyse the response from a histogram of either photon counts or integrated intensities to assess the number, positions and amplitudes of the reflected returns from target surfaces. The Markov chain Monte Carlo (MCMC) methodology, combined with a random sampling of the search space, enables us to detect and characterise both near and far targets from a fuller, more sensitive analysis than existing methods.

Original language	English
Title of host publication	Proceedings of the Ninth Conference on Machine Vision Applications
Subtitle of host publication	May 16-18, 2005, Tsukuba Science City, Japan
Publisher	The University of Tokyo
Pages	193-196
Number of pages	4
ISBN (Print)	4901122045, 9784901122047
Publication status	Published - 2005
Event	9th IAPR Conference on Machine Vision Applications 2005 - Tsukuba Science City, Japan Duration: 16 May 2005 → 18 May 2005

Conference

Conference	9th IAPR Conference on Machine Vision Applications 2005
Abbreviated title	MVA 2005
Country/Territory	Japan
City	Tsukuba Science City
Period	16/05/05 → 18/05/05

ASJC Scopus subject areas

Computer Vision and Pattern Recognition

Cite this

@inproceedings{63a5e2a7ae85472b9c962ea7fde3f5db,

title = "Markov chain monte carlo algorithms for 3D ranging and imaging",

abstract = "We propose a new approach for the processing of Time- Correlated Single Photon Count (TCSPC) and Burst Illumination Laser (BIL) data. This data can be used to measure range, surface shape and determine a characteristic signature for remote targets. In general, the problem is to analyse the response from a histogram of either photon counts or integrated intensities to assess the number, positions and amplitudes of the reflected returns from target surfaces. The Markov chain Monte Carlo (MCMC) methodology, combined with a random sampling of the search space, enables us to detect and characterise both near and far targets from a fuller, more sensitive analysis than existing methods.",

author = "Sergio Hernandez-Marin and Wallace, {Andrew Michael} and Gibson, {Gavin Jarvis}",

year = "2005",

language = "English",

isbn = "4901122045",

pages = "193--196",

booktitle = "Proceedings of the Ninth Conference on Machine Vision Applications",

publisher = "The University of Tokyo",

address = "Japan",

note = "9th IAPR Conference on Machine Vision Applications 2005, MVA 2005 ; Conference date: 16-05-2005 Through 18-05-2005",

}

Markov chain monte carlo algorithms for 3D ranging and imaging. / Hernandez-Marin, Sergio; Wallace, Andrew Michael; Gibson, Gavin Jarvis.

Proceedings of the Ninth Conference on Machine Vision Applications: May 16-18, 2005, Tsukuba Science City, Japan. The University of Tokyo, 2005. p. 193-196.

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

TY - GEN

T1 - Markov chain monte carlo algorithms for 3D ranging and imaging

AU - Hernandez-Marin, Sergio

AU - Wallace, Andrew Michael

AU - Gibson, Gavin Jarvis

PY - 2005

Y1 - 2005

N2 - We propose a new approach for the processing of Time- Correlated Single Photon Count (TCSPC) and Burst Illumination Laser (BIL) data. This data can be used to measure range, surface shape and determine a characteristic signature for remote targets. In general, the problem is to analyse the response from a histogram of either photon counts or integrated intensities to assess the number, positions and amplitudes of the reflected returns from target surfaces. The Markov chain Monte Carlo (MCMC) methodology, combined with a random sampling of the search space, enables us to detect and characterise both near and far targets from a fuller, more sensitive analysis than existing methods.

AB - We propose a new approach for the processing of Time- Correlated Single Photon Count (TCSPC) and Burst Illumination Laser (BIL) data. This data can be used to measure range, surface shape and determine a characteristic signature for remote targets. In general, the problem is to analyse the response from a histogram of either photon counts or integrated intensities to assess the number, positions and amplitudes of the reflected returns from target surfaces. The Markov chain Monte Carlo (MCMC) methodology, combined with a random sampling of the search space, enables us to detect and characterise both near and far targets from a fuller, more sensitive analysis than existing methods.

UR - http://www.scopus.com/inward/record.url?scp=33644523218&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:33644523218

SN - 4901122045

SN - 9784901122047

SP - 193

EP - 196

BT - Proceedings of the Ninth Conference on Machine Vision Applications

PB - The University of Tokyo

T2 - 9th IAPR Conference on Machine Vision Applications 2005

Y2 - 16 May 2005 through 18 May 2005

ER -

Markov chain monte carlo algorithms for 3D ranging and imaging

Abstract

Conference

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this