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Publications de C. Heipke
Résultat de la recherche dans la liste des publications :
Article |
1 - A Marked Point Process for Modeling Lidar Waveforms.
C. Mallet et F. Lafarge et M. Roux et U. Soergel et F. Bretar et C. Heipke. IEEE Trans. Image Processing, 19(12): pages 3204-3221, décembre 2010.
Mots-clés : Clustering algorithms, Image color analysis, Image edge detection, Image segmentation, Monte Carlo Sampling, Object-based stochastic model.
@ARTICLE{mallet_tip2010,
|
| author |
= |
{Mallet, C. and Lafarge, F. and Roux, M. and Soergel, U. and Bretar, F. and Heipke, C.}, |
| title |
= |
{A Marked Point Process for Modeling Lidar Waveforms}, |
| year |
= |
{2010}, |
| month |
= |
{décembre}, |
| journal |
= |
{IEEE Trans. Image Processing}, |
| volume |
= |
{19}, |
| number |
= |
{12}, |
| pages |
= |
{3204-3221}, |
| url |
= |
{http://dx.doi.org/10.1109/TIP.2010.2052825}, |
| keyword |
= |
{Clustering algorithms, Image color analysis, Image edge detection, Image segmentation, Monte Carlo Sampling, Object-based stochastic model} |
| } |
Abstract :
Lidar waveforms are 1D signals representing a train of echoes caused by reflections at different targets. Modeling these echoes with the appropriate parametric function is useful to retrieve information about the physical characteristics of the targets. This paper presents a new probabilistic model based on a marked point process which reconstructs the echoes from recorded discrete waveforms as a sequence of parametric curves. Such an approach allows to fit each mode of a waveform with the most suitable function and to deal with both, symmetric and asymmetric, echoes. The model takes into account a data term, which measures the coherence between the models and the waveforms, and a regularization term, which introduces prior knowledge on the reconstructed signal. The exploration of the associated configuration space is performed by a Reversible Jump Markov Chain Monte Carlo sampler coupled with simulated annealing. Experiments with different kinds of lidar signals, especially from urban scenes, show the high potential of the proposed approach. To further demonstrate the advantages of the suggested method, actual laser scans are classified and the results are reported. |
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Article de conférence |
1 - Lidar Waveform Modeling using a Marked Point Process.
C. Mallet et F. Lafarge et F. Bretar et U. Soergel et C. Heipke. Dans Proc. IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, novembre 2009.
Mots-clés : 3D point cloud, Lidar, Marked point process, RJMCMC.
@INPROCEEDINGS{mallet_icip09,
|
| author |
= |
{Mallet, C. and Lafarge, F. and Bretar, F. and Soergel, U. and Heipke, C.}, |
| title |
= |
{Lidar Waveform Modeling using a Marked Point Process}, |
| year |
= |
{2009}, |
| month |
= |
{novembre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Cairo, Egypt}, |
| url |
= |
{http://dx.doi.org/10.1109/ICIP.2009.5413380}, |
| keyword |
= |
{3D point cloud, Lidar, Marked point process, RJMCMC} |
| } |
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. |
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