Diffusions for global optimizations
SIAM Journal on Control and Optimization
Statistical Modeling of Texture Sketch
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Modelling and Interpretation of Architecture from Several Images
International Journal of Computer Vision
Range Image Segmentation by an Effective Jump-Diffusion Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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
Multiple target direction of arrival tracking
IEEE Transactions on Signal Processing
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This paper presents a new approach to describe images in terms of geometric objects. Methods based on conventional stochastic marked point processes have already led to convincing image analysis results but possess several drawbacks such as complex parameter tuning, large computing time, and lack of generality. We propose a generalized marked point process model which can be performed in shorter computing times and applied to a variety of applications without modifying the model or tuning parameters. In our approach, both linear and areal primitives extracted from a library of geometric objects are matched to a given image using a probabilistic Gibbs model. A Jump-Diffusion process is performed to find the optimal object configuration. Experiments with remotely sensed images show good potentialities of the proposed approach.