학술논문
Target detection for depth imaging using sparse single-photon data
Document Type
Conference
Source
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on. :3256-3260 Mar, 2016
Subject
Language
ISSN
2379-190X
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.