The nature of statistical learning theory
The nature of statistical learning theory
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Support vector density estimation
Advances in kernel methods
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facts, Conjectures, and Improvements for Simulated Annealing
Facts, Conjectures, and Improvements for Simulated Annealing
Modelling and Interpretation of Architecture from Several Images
International Journal of Computer Vision
Monitoring Usage of Workstations with a Relational Database
LISA '94 Proceedings of the 8th USENIX conference on System administration
Point Processes for Unsupervised Line Network Extraction in Remote Sensing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Building Outline Extraction from Digital Elevation Models Using Marked Point Processes
International Journal of Computer Vision
Bayesian Analysis of Lidar Signals with Multiple Returns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finite mixture of α-stable distributions
Digital Signal Processing
Structural Approach for Building Reconstruction from a Single DSM
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
Contribution of airborne full-waveform lidar and image data for urban scene classification
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Lidar waveform modeling using a marked point process
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Geometric Feature Extraction by a Multimarked Point Process
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive simulated annealing for energy minimization problem in a marked point process application
EMMCVPR'05 Proceedings of the 5th 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
Perfect sampling for the wavelet reconstruction of signals
IEEE Transactions on Signal Processing
Modeling SAR images with a generalization of the Rayleigh distribution
IEEE Transactions on Image Processing
SAR amplitude probability density function estimation based on a generalized Gaussian model
IEEE Transactions on Image Processing
Efficient monte carlo sampler for detecting parametric objects in large scenes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Detecting parametric objects in large scenes by Monte Carlo sampling
International Journal of Computer Vision
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Lidar waveforms are 1-D 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 upon 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 (RJMCMC) 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.