Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive mixture estimation and unsupervised local Bayesian image segmentation
Graphical Models and Image Processing
JESSICA: an object-oriented hypermedia publishing processor
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Estimation of generalized mixtures and its application in image segmentation
IEEE Transactions on Image Processing
Algorithm 781: generating Hilbert's space-filling curve by recursion
ACM Transactions on Mathematical Software (TOMS)
Unsupervised Multispectral Image Segmentation Using Generalized Gaussian Noise Model
EMMCVPR '99 Proceedings of the Second International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
MRF parameter estimation by an accelerated method
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
A Statistical Model for Contours in Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised signal restoration using hidden Markov chains with copulas
Signal Processing
Fuzzy Markov Random Fields versus Chains for Multispectral Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of color images via reversible jump MCMC sampling
Image and Vision Computing
Extension of higher-order HMC modeling with application to image segmentation
Digital Signal Processing
Multiband segmentation based on a hierarchical Markov model
Pattern Recognition
Pattern Recognition Letters
Unsupervised segmentation of hidden semi-Markov non-stationary chains
Signal Processing
Hi-index | 0.14 |
This paper attacks the problem of generalized multisensor mixture estimation. A distribution mixture is said to be generalized when the exact nature of components is not known, but each of them belongs to a finite known set of families of distributions. Estimating such a mixture entails a supplementary difficulty: One must label, for each class and each sensor, the exact nature of the corresponding distribution. Such generalized mixtures have been studied assuming that the components lie in the Pearson system. Adaptations of classical algorithms, such as Expectation-Maximization, Stochastic Expectation-Maximization, or Iterative Conditional Estimation, can then be used to estimate such mixtures in the context of independent identically distributed data and hidden Markov random fields. We propose a more general procedure with applications to estimating generalized multisensor hidden Markov chains. Our proposed method is applied to the problem of unsupervised image segmentation. The method proposed allows one to: (i) identify the conditional distribution for each class and each sensor, (ii) estimate the unknown parameters in this distribution, (iii) estimate priors, and (iv) estimate the "true" class image.