Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Study of Methods of Choosing the Smoothing Parameter in Image Restoration by Regularization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Texture Segmentation Using Markov Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constrained Restoration and the Recovery of Discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised segmentation of noisy and textured images using Markov random fields
CVGIP: Graphical Models and Image Processing
WBIA '98 Proceedings of the IEEE Workshop on Biomedical Image Analysis
Estimation of Markov random field prior parameters using Markov chain Monte Carlo maximum likelihood
IEEE Transactions on Image Processing
Optimal Level Curves and Global Minimizers of Cost Functionals in Image Segmentation
Journal of Mathematical Imaging and Vision
Level Lines as Global Minimizers of Energy Functionals in Image Segmentation
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
An Unsupervised Clustering Method Using the Entropy Minimization
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
A novel pixon-representation for image segmentation based on Markov random field
Image and Vision Computing
Deducing local influence neighbourhoods with application to edge-preserving image denoising
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
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The problem of extracting information from an image which corresponds to early stage processing in vision is addressed. We propose a new approach (the MPI approach) which simultaneously provides a restored image, a segmented image and a map which reflects the local scale for representing the information. Embedded in a Bayesian framework, this approach is based on an information prior, a pixon model and two Markovian priors. This model based approach is oriented to detect and analyze small parabolic patches in a noisy environment. The number of clusters and their parameters are not required for the segmentation process. The MPI approach is applied to the analysis of Statistical Parametric Maps obtained from fMRI experiments.