• Corpus ID: 16931285

Markov Random Field Image Models and Their Applications to Computer Vision

@inproceedings{Geman2010MarkovRF,
  title={Markov Random Field Image Models and Their Applications to Computer Vision},
  author={Stuart Geman},
  year={2010}
}
1. Introduction. Computer vision refers to a variety of applications involving a sensing device, a computer, and software for restoring and possibly interpreting the sensed data. Most commonly, visible light is sensed by a video camera and converted to an array of measured light intensities, each element corresponding to a small patch in the scene (a picture element, or "pixel"). The image is thereby "digitized," and this format is suitable for computer analysis. In some applications, the… 

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References

SHOWING 1-10 OF 32 REFERENCES
Simple Parallel Hierarchical and Relaxation Algorithms for Segmenting Noncausal Markovian Random Fields
  • F. CohenD. Cooper
  • Computer Science
    IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 1987
TLDR
Two conceptually new algorithms are presented for segmenting textured images into regions in each of which the data are modeled as one of C MRF's, designed to operate in real time when implemented on new parallel computer architectures that can be built with present technology.
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
  • S. GemanD. Geman
  • Physics
    IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 1984
TLDR
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.
Markov Random Field Texture Models
TLDR
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.
Image restoration using an estimated Markov model
A Renormalization Group Approach to Image Processing Problems
  • B. Gidas
  • Computer Science
    IEEE Trans. Pattern Anal. Mach. Intell.
  • 1989
TLDR
The restoration algorithm is a global-optimization algorithm applicable to other optimization problems, and generates iteratively a multilevel cascode of restored images corresponding to different levels of resolution, or scale.
Statistics, images, and pattern recognition
Data are increasingly being collected in the form of images, especially in fields using remote sensing and microscopy. Statisticians are becoming interested in developing techniques to handle the
Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
  • H. DerinH. Elliott
  • Computer Science
    IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 1987
TLDR
This paper presents random field models for noisy and textured image data based upon a hierarchy of Gibbs distributions, and presents dynamic programming based segmentation algorithms for chaotic images, considering a statistical maximum a posteriori (MAP) criterion.
Estimation of binary Markov random fields
TLDR
A new estimation procedure is suggested that is analogous to minimum logit chi-squareestimation in logistic regression, does not involve non-linearoptimization, and decisively beats Maximum Pseudo-Likelihood in mean squarederror.
Probabilistic Solution of Ill-Posed Problems in Computational Vision
TLDR
This work derives efficient algorithms and describes parallel implementations on digital parallel SIMD architectures, as well as a new class of parallel hybrid computers that mix digital with analog components.
Statistical Analysis of Non-Lattice Data
In rather formal terms, the situation with which this paper is concerned may be described as follows. We are given a fixed system of n sites, labelled by the first n positive integers, and an
...
...