Point Processes for Unsupervised Line Network Extraction in Remote Sensing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Analysis of Lidar Signals with Multiple Returns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Marked point process for vascular tree extraction on angiogram
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Bayesian curve fitting using MCMC with applications to signalsegmentation
IEEE Transactions on Signal Processing
A marked point process for modeling lidar waveforms
IEEE Transactions on Image Processing
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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.