Journal ArticleDOI
Parameter identification for noisy image via the EM algorithm
Tohru Katayama,T. Hirai +1 more
TLDR
In this article, the EM algorithm is applied to each scalar subsystem derived from the state-space model via the discrete sine transform (DST) to obtain a scheme of estimating the AR parameters of transformed image.About:
This article is published in Signal Processing.The article was published on 1990-05-01. It has received 10 citations till now. The article focuses on the topics: Image processing & Parameter identification problem.read more
Citations
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Journal ArticleDOI
Radial basis function networks, regression weights, and the expectation-maximization algorithm
Reza Langari,Liang Wang,John Yen +2 more
TL;DR: A modified radial basis function (RBF) network in which the regression weights are used to replace the constant weights in the output layer is proposed and it is shown that the modified RBF network can reduce the number of hidden units significantly.
Proceedings ArticleDOI
A decomposition approach for fuzzy systems identification
Liang Wang,Reza Langari +1 more
TL;DR: The premise identification and the consequence identification of the model can be separated through use of the fuzzy discretization technique, while these are mutually related in previous methods, and the complexity of this approach is essentially unaffected by the number of fuzzy rules.
Journal ArticleDOI
Sugeno model, fuzzy discretization, and the EM algorithm
Liang Wang,Reza Langari +1 more
TL;DR: This approach consists of a fuzzy discretization technique used to determine the membership functions of input variables, which is the most difficult aspect in constructing a Sugeno-type model, and an iterative algorithm used to estimate the parameters of linear regression models in the consequent part of the model.
Journal ArticleDOI
Identification of image and blur parameters in frequency domain using the EM algorithm
TL;DR: A new approach is proposed to identify both the causal and semicausal AR parameters and blur parameters without a priori knowledge of the observation noise power and the PSF of the degradation of images degraded by observation noise only.
Proceedings ArticleDOI
A modified RBF network with application to system identification
Reza Langari,L. Wang +1 more
TL;DR: It is shown that the modified RBF network can reduce the number of hidden units significantly and a computationally efficient algorithm is developed, known as the EM algorithm, to estimate the parameters of the regression weights.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Journal Article
Optimal Filtering
TL;DR: This book helps to fill the void in the market and does that in a superb manner by covering the standard topics such as Kalman filtering, innovations processes, smoothing, and adaptive and nonlinear estimation.
Journal ArticleDOI
An approach to time series smoothing and forecasting using the em algorithm
TL;DR: In this article, an approach to smoothing and forecasting for time series with missing observations is proposed, where the EM algorithm is used in conjunction with the conventional Kalman smoothed estimators to derive a simple recursive procedure for estimating the parameters.
Journal ArticleDOI
Advances in mathematical models for image processing
TL;DR: Several state-of-the-art mathematical models useful in image processing are considered, including the traditional fast unitary transforms, autoregessive and state variable models as well as two-dimensional linear prediction models.
Journal ArticleDOI
Identification of image and blur parameters for the restoration of noncausal blurs
TL;DR: An optimal statistical parameter estimation technique is presented for the identification of unknown image and blur model parameters and the proposed algorithms constitute a generalization of previous work on blur identification in that they are able to locate the zero loci of the blurred image spectrum on the entire z 1 - z 2 plane.