Topic
Iterated conditional modes
About: Iterated conditional modes is a research topic. Over the lifetime, 248 publications have been published within this topic receiving 10725 citations.
Papers published on a yearly basis
Papers
TL;DR: In this paper, the authors proposed an iterative method for scene reconstruction based on a non-degenerate Markov Random Field (MRF) model, where the local characteristics of the original scene can be represented by a nondegenerate MRF and the reconstruction can be estimated according to standard criteria.
Abstract: may 7th, 1986, Professor A. F. M. Smith in the Chair] SUMMARY A continuous two-dimensional region is partitioned into a fine rectangular array of sites or "pixels", each pixel having a particular "colour" belonging to a prescribed finite set. The true colouring of the region is unknown but, associated with each pixel, there is a possibly multivariate record which conveys imperfect information about its colour according to a known statistical model. The aim is to reconstruct the true scene, with the additional knowledge that pixels close together tend to have the same or similar colours. In this paper, it is assumed that the local characteristics of the true scene can be represented by a nondegenerate Markov random field. Such information can be combined with the records by Bayes' theorem and the true scene can be estimated according to standard criteria. However, the computational burden is enormous and the reconstruction may reflect undesirable largescale properties of the random field. Thus, a simple, iterative method of reconstruction is proposed, which does not depend on these large-scale characteristics. The method is illustrated by computer simulations in which the original scene is not directly related to the assumed random field. Some complications, including parameter estimation, are discussed. Potential applications are mentioned briefly.
4,841 citations
TL;DR: In many cases, complete-data maximum likelihood estimation is relatively simple when conditional on some function of the parameters being estimated as mentioned in this paper, and convergence is stable, with each iteration increasing the likelihood.
Abstract: Two major reasons for the popularity of the EM algorithm are that its maximum step involves only complete-data maximum likelihood estimation, which is often computationally simple, and that its convergence is stable, with each iteration increasing the likelihood. When the associated complete-data maximum likelihood estimation itself is complicated, EM is less attractive because the M-step is computationally unattractive. In many cases, however, complete-data maximum likelihood estimation is relatively simple when conditional on some function of the parameters being estimated
2,021 citations
TL;DR: Describes a fully-automatic three-dimensional (3-D)-segmentation technique for brain magnetic resonance (MR) images that captures three features that are of special importance for MR images, i.e., nonparametric distributions of tissue intensities, neighborhood correlations, and signal inhomogeneities.
Abstract: We describe a fully-automatic 3D-segmentation technique for brain MR images. Using Markov random fields the segmentation algorithm captures three important MR features, i.e. non-parametric distributions of tissue intensities, neighborhood correlations and signal inhomogeneities. Detailed simulations and real MR images demonstrate the performance of the segmentation algorithm. The impact of noise, inhomogeneity, smoothing and structure thickness is analyzed quantitatively. Even single echo MR images are well classified into gray matter, white matter, cerebrospinal fluid, scalp-bone and background. A simulated annealing and an iterated conditional modes implementation are presented.
Keywords: Magnetic Resonance Imaging, Segmentation, Markov Random Fields
536 citations
16 Mar 2009
TL;DR: An iterated conditional modes algorithm is presented that rivals existing state-of-the-art NMF algorithms on an image feature extraction problem and discusses how the Gibbs sampler can be used for model order selection by estimating the marginal likelihood.
Abstract: We present a Bayesian treatment of non-negative matrix factorization (NMF), based on a normal likelihood and exponential priors, and derive an efficient Gibbs sampler to approximate the posterior density of the NMF factors On a chemical brain imaging data set, we show that this improves interpretability by providing uncertainty estimates We discuss how the Gibbs sampler can be used for model order selection by estimating the marginal likelihood, and compare with the Bayesian information criterion For computing the maximum a posteriori estimate we present an iterated conditional modes algorithm that rivals existing state-of-the-art NMF algorithms on an image feature extraction problem
246 citations
TL;DR: Three different statistical approaches to tackle automatic land cover classification from satellite images are considered: two of them, namely the well-known maximum likelihood classification and the support vector machine (SVM), are noncontextual methods, and iterated conditional modes (ICM), exploits spatial context by using a Markov random field.
198 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 2 |
| 2020 | 3 |
| 2019 | 4 |
| 2018 | 4 |
| 2017 | 8 |
| 2016 | 7 |