Target detection for depth imaging using sparse single-photon data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 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 languageEnglish
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
Pages3256-3260
Number of pages5
ISBN (Print)9781479999880
DOIs
Publication statusPublished - 2016
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing 2016 - Shanghai International Convention Center, Shanghai, China
Duration: 20 Mar 201625 Mar 2016

Conference

Conference41st IEEE International Conference on Acoustics, Speech and Signal Processing 2016
Abbreviated titleICASSP 2016
CountryChina
CityShanghai
Period20/03/1625/03/16

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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