Topic
Expectation–maximization algorithm
About: Expectation–maximization algorithm is a research topic. Over the lifetime, 11823 publications have been published within this topic receiving 528693 citations. The topic is also known as: EM algorithm & Expectation Maximization.
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341 citations
TL;DR: This paper formulates a corresponding expectation-maximization (EM) algorithm, as well as a method for estimating noise properties at the ML estimate, for an idealized two-dimensional positron emission tomography [2-D PET] detector.
Abstract: Using a theory of list-mode maximum-likelihood (ML) source reconstruction presented recently by Barrett et al. (1997), this paper formulates a corresponding expectation-maximization (EM) algorithm, as well as a method for estimating noise properties at the ML estimate. List-mode ML is of interest in cases where the dimensionality of the measurement space impedes a binning of the measurement data. It can be advantageous in cases where a better forward model can be obtained by including more measurement coordinates provided by a given detector. Different figures of merit for the detector performance can be computed from the Fisher information matrix (FIM). This paper uses the observed FIM, which requires a single data set, thus, avoiding costly ensemble statistics. The proposed techniques are demonstrated for an idealized two-dimensional (2-D) positron emission tomography (PET) [2-D PET] detector. The authors compute from simulation data the improved image quality obtained by including the time of flight of the coincident quanta.
341 citations
13 Jun 2000
TL;DR: A method is presented to recover 3D scene structure and camera motion from multiple images without the need for correspondence information by means of an algorithm which iteratively refines a probability distribution over the set of all correspondence assignments.
Abstract: A method is presented to recover 3D scene structure and camera motion from multiple images without the need for correspondence information. The problem is framed as finding the maximum likelihood structure and motion given only the 2D measurements, integrating over all possible assignments of 3D features to 2D measurements. This goal is achieved by means of an algorithm which iteratively refines a probability distribution over the set of all correspondence assignments. At each iteration a new structure from motion problem is solved, using as input a set of 'virtual measurements' derived from this probability distribution. The distribution needed can be efficiently obtained by Markov Chain Monte Carlo sampling. The approach is cast within the framework of Expectation-Maximization, which guarantees convergence to a local maximizer of the likelihood. The algorithm works well in practice, as will be demonstrated using results on several real image sequences.
340 citations
TL;DR: A unified information geometrical framework for studying stochastic models of neural networks, by focusing on the EM and em algorithms, and proves a condition that guarantees their equivalence.
339 citations
TL;DR: This paper compares the efficacy of five current, and promising, methods that can be used to deal with missing data and concludes that MI, because of its theoretical and distributional underpinnings, is probably most promising for future applications in this field.
338 citations