Details
| Original language | English |
|---|---|
| Title of host publication | 2009 IEEE International Conference on Image Processing, ICIP 2009 |
| Subtitle of host publication | Proceedings |
| Publisher | IEEE Computer Society |
| Pages | 1713-1716 |
| Number of pages | 4 |
| ISBN (print) | 9781424456543 |
| Publication status | Published - 17 Feb 2010 |
| Event | 2009 IEEE International Conference on Image Processing, ICIP 2009 - Cairo, Egypt Duration: 7 Nov 2009 → 10 Nov 2009 |
Publication series
| Name | Proceedings - International Conference on Image Processing, ICIP |
|---|---|
| ISSN (Print) | 1522-4880 |
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.
Keywords
- 3D point cloud, Lidar, Marked point process, RJMCMC, Signal reconstruction, Source modeling
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Signal Processing
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2009 IEEE International Conference on Image Processing, ICIP 2009 : Proceedings. IEEE Computer Society, 2010. p. 1713-1716 5413380 (Proceedings - International Conference on Image Processing, ICIP).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Lidar waveform modeling using a marked point process
AU - Mallet, Clément
AU - Lafarge, Florent
AU - Bretar, Frédéric
AU - Soergel, Uwe
AU - Heipke, Christian
PY - 2010/2/17
Y1 - 2010/2/17
N2 - 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.
AB - 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.
KW - 3D point cloud
KW - Lidar
KW - Marked point process
KW - RJMCMC
KW - Signal reconstruction
KW - Source modeling
UR - http://www.scopus.com/inward/record.url?scp=77951959562&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2009.5413380
DO - 10.1109/ICIP.2009.5413380
M3 - Conference contribution
AN - SCOPUS:77951959562
SN - 9781424456543
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 1713
EP - 1716
BT - 2009 IEEE International Conference on Image Processing, ICIP 2009
PB - IEEE Computer Society
T2 - 2009 IEEE International Conference on Image Processing, ICIP 2009
Y2 - 7 November 2009 through 10 November 2009
ER -