Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction

By G. Winkler. 2002

605 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/72b079f6c8820012cb70dc0d12e45a4a7b22bc30

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. M. A. Carreira-Perpiñán
2. Jessica J. Fridrich
3. Vojtech Holub
4. Yoshua Bengio
5. Foster J. Provost
6. Tomáš Denemark
7. Max Welling
8. Christophe Andrieu

In Computer Vision and Pattern Recognition

1. Vojtech Holub
2. Jessica J. Fridrich
3. M. A. Carreira-Perpiñán
4. Tomáš Denemark
5. Geoffrey E. Hinton
6. Luc Van Gool
7. Larry S. Davis
8. Jitendra Malik

In Statistics and Probability

1. Christophe Andrieu
2. John A. Thoms
3. M. A. Carreira-Perpiñán
4. M. V. Lieshout
5. Geoffrey E. Hinton
6. Jessica J. Fridrich
7. Foster J. Provost
8. Christian Gaser

Citations in the corpus

• Classification in Networked Data: A Toolkit and a Univariate Case Study (2007, positive): “Introductions to MRFs fill textbooks (Winkler, 2003); for our purposes, it is important to point out that they are the basis both directly and indirectly for many network classification approaches.”
• Digital image steganography using universal distortion (2013, methodology): “3This notation was used in [3] and is also standard in the literature on Markov random fields [32].”
• A tutorial on adaptive MCMC (2008, background): “In fact, as pointed out to us by Y.F. Atchadé, an example in Winkler (2003) shows that even in the situation where θi(1) = θi(2) = 1 − 1/i, the sequence {n−1∑ni=1Xi − 3/2} does not vanish (in the mean square sense) as i → ∞.”
• Universal distortion function for steganography in an arbitrary domain (2014, methodology): “gThis notation was used in [4] and is also standard in the literature on Markov random fields [32].”
• An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis (2012, background): “We will briefly summarize the important aspects of MRFs that have been extensively reviewed elsewhere (Winkler, 2003).”
• On Contrastive Divergence Learning (2005, background): “This class of random-field distributions has found many practical applications (Li, 2001; Winkler, 2002; Teh et al., 2003; He et al., 2004).”
• On Estimation of the Intensity Function of a Point Process (2011, methodology): “Smoothing between nearby intensity values is achieved by means of a Markov random field prior in the spirit of Bayesian image analysis (Winkler 2003).”