Parameter estimation and model selection in image analysis using Gibbs-Markov random fields
Parameter estimation and model selection in image analysis using Gibbs-Markov random fields
Texture Modeling by Multiple Pairwise Pixel Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast texture synthesis using tree-structured vector quantization
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
I3D '01 Proceedings of the 2001 symposium on Interactive 3D graphics
FRAME: Filters, Random fields, and Minimax Entropy-- Towards a Unified Theory for Texture Modeling
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Nonparametric Markov Random Field Model Analysis of the MeasTex Test Suite
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Texture synthesis via a noncausal nonparametric multiscale Markov random field
IEEE Transactions on Image Processing
ACM SIGGRAPH 2007 courses
Texture classification using sparse frame-based representations
EURASIP Journal on Applied Signal Processing
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Abstract--The strong Markov random field (strong-MRF) model is a submodel of the more general MRF-Gibbs model. The strong-MRF model defines a system whose field is Markovian with respect to a defined neighborhood, and all subneighborhoods are also Markovian. A checkerboard pattern is a perfect example of a strong Markovian system. Although the strong Markovian system requires a more stringent assumption about the field, it does have some very nice mathematical properties. One mathematical property is the ability to define the strong-MRF model with respect to its marginal distributions over the cliques. Also, a direct equivalence to the Analysis-of-Variance (ANOVA) log-linear construction can be proven. From this proof, the general ANOVA log-linear construction formula is acquired.