Target detection for depth imaging using sparse single-photon data
Yoann Altmann, Ximing Ren, Aongus McCarthy, Gerald S. Buller, Steve McLaughlin
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.
BibTeX
@inproceedings{icassp2016_targetdetectionf,
title = {Target detection for depth imaging using sparse single-photon data},
author = {Yoann Altmann and Ximing Ren and Aongus McCarthy and Gerald S. Buller and Steve McLaughlin},
booktitle = {ICASSP 2016},
year = {2016}
}