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 proceedingConference contribution

8 Citations (Scopus)


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 languageEnglish
Title of host publicationProceedings of the Ninth Conference on Machine Vision Applications
Subtitle of host publicationMay 16-18, 2005, Tsukuba Science City, Japan
PublisherThe University of Tokyo
Number of pages4
ISBN (Print)4901122045, 9784901122047
Publication statusPublished - 2005
Event9th IAPR Conference on Machine Vision Applications 2005 - Tsukuba Science City, Japan
Duration: 16 May 200518 May 2005


Conference9th IAPR Conference on Machine Vision Applications 2005
Abbreviated titleMVA 2005
CityTsukuba Science City

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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