Journal ArticleDOI
Two-Dimensional Discrete Gaussian Markov Random Field Models for Image Processing
TLDR
The notion of Markovianity on a plane, statistical inference in GMRF models, and their applications in several image related problems such as, image synthesis, texture classification, segmentation and image restoration are discussed.Abstract:
This paper is concerned with a systematic exposition of the usefulness of two-dimensional (2-D) discrete Gaussian Markov random field (GMRF) models for image processing applications. Specifically, we discuss the following topics; notion of Markovianity on a plane, statistical inference in GMRF models, and their applications in several image related problems such as, image synthesis, texture classification, segmentation and image restoration.read more
Citations
More filters
Book
Markov Random Field Modeling in Image Analysis
TL;DR: This detailed and thoroughly enhanced third edition presents a comprehensive study / reference to theories, methodologies and recent developments in solving computer vision problems based on MRFs, statistics and optimisation.
Journal ArticleDOI
Texture analysis and classification with tree-structured wavelet transform
T. Chang,C.-C.J. Kuo +1 more
TL;DR: A progressive texture classification algorithm which is not only computationally attractive but also has excellent performance is developed and is compared with that of several other methods.
Journal ArticleDOI
Texture classification and segmentation using multiresolution simultaneous autoregressive models
Jianchang Mao,Anil K. Jain +1 more
TL;DR: It is demonstrated that integrating the information extracted from multiresolution SAR models gives much better performance than single resolution methods in both texture classification and texture segmentation.
Journal ArticleDOI
Iterative methods for image deblurring
TL;DR: In this paper, the authors discuss the use of iterative restoration algorithms for the removal of linear blurs from photographic images that may also be degraded by pointwise nonlinearities such as film saturation and additive noise.
Journal ArticleDOI
Random field models in image analysis
Richard C. Dubes,Anil K. Jain +1 more
TL;DR: This review paper explains how Gibbs and Markov random field models provide a unifying theme for many contemporary problems in image analysis and allows the introduction of spatial context into pixel labeling problems, such as segmentation and restoration.
References
More filters
Journal ArticleDOI
Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Journal ArticleDOI
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Journal ArticleDOI
Statistical and structural approaches to texture
TL;DR: This survey reviews the image processing literature on the various approaches and models investigators have used for texture, including statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models.
A comparative study of texture measures for terrain classification.
J. S. Weszka,A. Rosenfeld +1 more
TL;DR: Three standard approaches to automatic texture classification make use of features based on the Fourier power spectrum, on second-order gray level statistics, and on first-order statistics of gray level differences, respectively; it was found that the Fouriers generally performed more poorly, while the other feature sets all performned comparably.
Journal ArticleDOI
Markov Random Field Texture Models
George R. Cross,Anil K. Jain +1 more
TL;DR: The power of the binomial model to produce blurry, sharp, line-like, and blob-like textures is demonstrated and the synthetic microtextures closely resembled their real counterparts, while the regular and inhomogeneous textures did not.