Abstract
This paper presents a new Bayesian model and algorithm used for depth and reflectivity profiling using full waveforms from the time-correlated single-photon counting measurement in the limit of very low photon counts. The proposed model represents each Lidar waveform as a combination of a known impulse response, weighted by the target reflectivity, and an unknown constant background, corrupted by Poisson noise. Prior knowledge about the problem is embedded through prior distributions that account for the different parameter constraints and their spatial correlation among the image pixels. In particular, a gamma Markov random field (MRF) is used to model the joint distribution of the target reflectivity, and a second MRF is used to model the distribution of the target depth, which are both expected to exhibit significant spatial correlations. An adaptive Markov chain Monte Carlo algorithm is then proposed to perform Bayesian inference. This algorithm is equipped with a stochastic optimization adaptation mechanism that automatically adjusts the parameters of the MRFs by maximum marginal likelihood estimation. Finally, the benefits of the proposed methodology are demonstrated through a series of experiments using real data.
Original language | English |
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Pages (from-to) | 1935-1946 |
Number of pages | 12 |
Journal | IEEE Transactions on Image Processing |
Volume | 25 |
Issue number | 5 |
DOIs | |
Publication status | Published - May 2016 |
Keywords
- stat.AP
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Dive into the research topics of 'Lidar Waveform-Based Analysis of Depth Images Constructed Using Sparse Single-Photon Data'. Together they form a unique fingerprint.Profiles
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Yoann Altmann
- School of Engineering & Physical Sciences - Professor
- School of Engineering & Physical Sciences, Institute of Sensors, Signals & Systems - Professor
Person: Academic (Research & Teaching)
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Stephen McLaughlin
- School of Engineering & Physical Sciences, Institute of Sensors, Signals & Systems - Professor
- School of Engineering & Physical Sciences - Professor
Person: Academic (Research & Teaching)