Finding road seeds in aerial images
CVGIP: Image Understanding
An Active Testing Model for Tracking Roads in Satellite Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Finding of Main Roads in Aerial Images by Using Geometric-Stochastic Models and Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
New Prospects in Line Detection by Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Automatic extraction of roads from aerial images based on scale space and snakes
Machine Vision and Applications
The Candy model revisited: Markov properties and inference
The Candy model revisited: Markov properties and inference
A Gibbs Point Process for Road Extraction from Remotely Sensed Images
International Journal of Computer Vision
A Geometric Primitive Extraction Process for Remote Sensing Problems
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Object Extraction Using a Stochastic Birth-and-Death Dynamics in Continuum
Journal of Mathematical Imaging and Vision
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Prediction and change detection in sequential data for interactive applications
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Unsupervised line network extraction in remote sensing using a polyline process
Pattern Recognition
Extended Phase Field Higher-Order Active Contour Models for Networks
International Journal of Computer Vision
Detection of individual specimens in populations using contour energies
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
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
Lidar waveform modeling using a marked point process
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A marked point process for modeling lidar waveforms
IEEE Transactions on Image Processing
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Multi-scale bayesian based horizon matchings across faults in 3d seismic data
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
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
International Journal of Computer Vision and Image Processing
Detecting parametric objects in large scenes by Monte Carlo sampling
International Journal of Computer Vision
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This paper addresses the problem of unsupervised extraction of line networks (for example, road or hydrographic networks) from remotely sensed images. We model the target line network by an object process, where the objects correspond to interacting line segments. The prior model, called "Quality Candy,驴 is designed to exploit as fully as possible the topological properties of the network under consideration, while the radiometric properties of the network are modeled using a data term based on statistical tests. Two techniques are used to compute this term: one is more accurate, the other more efficient. A calibration technique is used to choose the model parameters. Optimization is done via simulated annealing using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. We accelerate convergence of the algorithm by using appropriate proposal kernels. The results obtained on satellite and aerial images are quantitatively evaluated with respect to manual extractions. A comparison with the results obtained using a previous model, called the "Candy驴 model, shows the interest of adding quality coefficients with respect to interactions in the prior density. The relevance of using an offline computation of the data potential is shown, in particular, when a proposal kernel based on this computation is added in the RJMCMC algorithm.