Name
Abstract | ||
|---|---|---|
Lidar waveforms are 1D signal consisting of a train of echoes where each of them correspond to a scattering target of the Earth surface. Modeling these echoes with the appropriate parametric function is necessary to retrieve physical information about these objects and characterize their properties. This paper presents a marked point process based model to reconstruct a lidar signal in terms of a set of parametric functions. The model takes into account both a data term which measures the coherence between the models and the waveforms, and a regularizing term which introduces physical knowledge on the reconstructed signal. We search for the best configuration of functions by performing a Reversible Jump Markov Chain Monte Carlo sampler coupled with a simulated annealing. Results are finally presented on different kinds of signals in urban areas. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICIP.2009.5413380 | ICIP |
Keywords | Field | DocType |
Markov processes,Monte Carlo methods,optical radar,radar signal processing,signal reconstruction,simulated annealing,1D signal,Earth surface,LIDAR signal reconstruction,LIDAR waveform modeling,echoes train,marked point process,parametric functions,physical information,reversible jump Markov chain Monte Carlo sampler,scattering target,simulated annealing,3D point cloud,Lidar,Marked point process,RJMCMC,Signal reconstruction,Source modeling | Simulated annealing,Computer vision,Parametric equation,Monte Carlo method,Markov process,Pattern recognition,Computer science,Waveform,Reversible-jump Markov chain Monte Carlo,Lidar,Artificial intelligence,Signal reconstruction | Conference |
Citations | PageRank | References |
1 | 0.39 | 9 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Clément Mallet | 1 | 134 | 15.93 |
| Florent Lafarge | 2 | 387 | 25.70 |
| Frédéric Bretar | 3 | 24 | 5.30 |
| Uwe Soergel | 4 | 209 | 22.68 |
| Christian Heipke | 5 | 111 | 20.74 |