A random model for analyzing region quadtrees
Pattern Recognition Letters
High-order statistical texture analysis--font recognition applied
Pattern Recognition Letters
A clustering method based on multidimensional texture analysis
Pattern Recognition
Texture classification using ridgelet transform
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Fast and active texture segmentation based on orientation and local variance
Journal of Visual Communication and Image Representation
Rotation-invariant and scale-invariant Gabor features for texture image retrieval
Image and Vision Computing
Wavelet and curvelet moments for image classification: Application to aggregate mixture grading
Pattern Recognition Letters
Statistical pattern recognition in remote sensing
Pattern Recognition
Image retrieval based on the texton co-occurrence matrix
Pattern Recognition
On texture and image interpolation using Markov models
Image Communication
Texture image retrieval based on non-tensor product wavelet filter banks
Signal Processing
Texture metamorphosis driven by texton masks
Computers and Graphics
Colour, texture, and motion in level set based segmentation and tracking
Image and Vision Computing
Skin detection using pairwise models
Image and Vision Computing
Markov random field approach to region extraction using Tabu Search
Journal of Visual Communication and Image Representation
Foveation embedded DCT domain video transcoding
Journal of Visual Communication and Image Representation
Evolving descriptors for texture segmentation
Pattern Recognition
Automated vision system for localizing structural defects in textile fabrics
Pattern Recognition Letters
Image retrieval based on micro-structure descriptor
Pattern Recognition
A review on automatic image annotation techniques
Pattern Recognition
Statistical priors for efficient combinatorial optimization via graph cuts
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Image segmentation using local spectral histograms and linear regression
Pattern Recognition Letters
Analysis and modelling of diversity contribution to ensemble-based texture recognition performance
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Rotation-Invariant texture classification using steerable gabor filter bank
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Bayesian image segmentation using gaussian field priors
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Extended local binary patterns for texture classification
Image and Vision Computing
Scale and orientation matching for texture analysis and recognition
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
Selective extraction of entangled textures via adaptive PDE transform
Journal of Biomedical Imaging - Special issue on Mathematical Methods for Images and Surfaces 2011
Bayesian models for medical image biology using monte carlo markov chains techniques
Mathematical and Computer Modelling: An International Journal
Geometrically Guided Exemplar-Based Inpainting
SIAM Journal on Imaging Sciences
Content-based image retrieval using color difference histogram
Pattern Recognition
Texture segmentation using different orientations of GLCM features
Proceedings of the 6th International Conference on Computer Vision / Computer Graphics Collaboration Techniques and Applications
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We consider a texture to be a stochastic, possibly periodic, two-dimensional image field. A texture model is a mathematical procedure capable of producing and describing a textured image. We explore the use of Markov random fields as texture models. The binomial model, where each point in the texture has a binomial distribution with parameter controlled by its neighbors and ``number of tries'' equal to the number of gray levels, was taken to be the basic model for the analysis. A method of generating samples from the binomial model is given, followed by a theoretical and practical analysis of the method's convergence. Examples show how the parameters of the Markov random field control the strength and direction of the clustering in the image. The power of the binomial model to produce blurry, sharp, line-like, and blob-like textures is demonstrated. Natural texture samples were digitized and their parameters were estimated under the Markov random field model. A hypothesis test was used for an objective assessment of goodness-of-fit under the Markov random field model. Overall, microtextures fit the model well. The estimated parameters of the natural textures were used as input to the generation procedure. The synthetic microtextures closely resembled their real counterparts, while the regular and inhomogeneous textures did not.