Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gauss-Markov Measure Field Models for Low-Level Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Color image segmentation using fuzzy C-means and eigenspace projections
Signal Processing
Interactive Organ Segmentation Using Graph Cuts
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
The MPM-MAP Algorithm for Image Segmentation
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Hidden Markov Measure Field Models for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A General Bayesian Markov Random Field Model for Probabilistic Image Segmentation
IWCIA '09 Proceedings of the 13th International Workshop on Combinatorial Image Analysis
Variational image segmentation using boundary functions
IEEE Transactions on Image Processing
Entropy-Controlled Quadratic Markov Measure Field Models for Efficient Image Segmentation
IEEE Transactions on Image Processing
| Hi-index | 5.23 |
We apply the theory of metric-divergences between probability distributions and a variational approach in order to obtain a new model for probabilistic image segmentation. We study a specific model based on a very general measure between discrete probability distributions. We show experimentally that this model is competitive with some other models of the state of the art. In this work we use a particular case of the the measure of kind(@a@b@c@d) between two discrete probability distributions.