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
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Gibbs Point Process for Road Extraction from Remotely Sensed Images
International Journal of Computer Vision
Point Processes for Unsupervised Line Network Extraction in Remote Sensing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Contours with Free Endpoints
Journal of Mathematical Imaging and Vision
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Marked point processes provide a rigorous framework to describe a scene by an unordered set of objects. The efficiency of this modeling has been shown on line network extraction with models manipulating interacting segments. In this paper, we extend this previous modeling to polylines composed of an unknown number of segments. Optimization is done via simulated annealing using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. We accelerate the convergence of the algorithm by using appropriate proposal kernels. Results on aerial and satellite images show that this new model outperforms the previous one.