Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Frozen-State hierarchical annealing
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Posterior sampling of scientific images
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Sonar image segmentation using an unsupervised hierarchical MRF model
IEEE Transactions on Image Processing
Parallel Hidden Hierarchical Fields for Multi-scale Reconstruction
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Virtual Super Resolution of Scale Invariant Textured Images Using Multifractal Stochastic Processes
Journal of Mathematical Imaging and Vision
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In any image processing involving images having scale-dependent structure, a key challenge is the modeling of these multi-scale characteristics. Because single Gauss-Markov models are effective at representing only single-scale phenomena, the classic Hidden Markov Model can not perform well in the processing of more complex images, particularly near-fractal images which frequently occur in scientific imaging. Of further interest is the presence of space-variable, nonstationary behaviour. By constructing hierarchical hidden fields, which label the behaviour type, we are able to capture heterogeneous structure in a scale-dependent way. We will illustrate the approach with a method of frozen-state simulated annealing and will apply it to the resolution enhancement of porous media images.