Results 211 to 220 of about 17,756,051 (243)

Texture modeling by multiple pairwise pixel interactions

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996
A Markov random field model with a Gibbs probability distribution (GPD) is proposed for describing particular classes of grayscale images which can be called spatially uniform stochastic textures. The model takes into account only multiple short- and long-range pairwise interactions between the gray levels in the pixels. An effective learning scheme is
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Interacting Multiple Model LK Tracking

Applied Mechanics and Materials, 2014
The nonlinear motion state of object seriously affects the object tracking characteristics in complex motion scene. In this paper, we propose an interacting multiple model LK (IMM-LK) tracking algorithm to enhance the performance of tracking nonlinear moving object.
Hong Wang, Jia Deng
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Testing Subhypotheses in the Multiplicative Interaction Model

Technometrics, 1981
The problem of analyzing a two-way cross-classified treatment structure with only one observation per treatment combination is considered. A test procedure is given that will enable the data analyst to determine subareas of the data in which the data are additive. The procedure is developed by assuming that a multiplicative interaction model adequately
Mervyn G. Marasinghe, Dallas E. Johnson
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Forecasting volatility with interacting multiple models

Finance Research Letters, 2017
Abstract We examine the performance of Kalman filter techniques in forecasting volatility. We find that the simple implementation of an online Kalman filtering procedure that combines commonly used forecasting models with market-based estimates improves the accuracy of volatility forecasts.
Jiri Svec, Xerxis Katrak
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Asymptotic variances for the multiplicative interaction model

Journal of Applied Statistics, 1991
When modelling two-way analysis of variance interactions by a multiplicative term-[Formula] asymptotic variances and covariances are derived for the parameters p, yi and Sj using maximum likelihood theory. The asymptotic framework is defined by a2/K where K is the number of observations per combination of the two factors and a2 the common variance of ...
Chadoeuf, Joel, J., Denis, J.B.
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Deep Interacting Multiple Model Filtering

2022 American Control Conference (ACC), 2022
Ghananeel Rotithor, Ashwin P. Dani
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Models of Multiple Interactions from Collinear Patterns

2018
Each collinear pattern should be made up of a large number of feature vectors which are located on a plane in a multidimensional feature space. Data subset located on a plane can represent linear interactions between multiple variables (features, genes).
Leon Bobrowski, Pawel Zabielski
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Interacting multiple model particle filter

IEE Proceedings - Radar, Sonar and Navigation, 2003
A new method for multiple model particle filtering for Markovian switching systems is presented. This new method is a combination of the interacting multiple model (IMM) filter and a (regularised) particle filter. The mixing and interaction is similar to that in a conventional IMM filter. However, in every mode a regularised particle filter is running.
Y. Boers, J.N. Driessen
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Analysis and application of opinion model with multiple topic interactions

Chaos: An Interdisciplinary Journal of Nonlinear Science, 2017
To reveal heterogeneous behaviors of opinion evolution in different scenarios, we propose an opinion model with topic interactions. Individual opinions and topic features are represented by a multidimensional vector. We measure an agent's action towards a specific topic by the product of opinion and topic feature.
Fei Xiong, Yun Liu, Liang Wang, Ximeng Wang   +3 more
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Multiple Models — Fixed, Switching, Interacting

2004
In dynamic models the dynamic and the observation equations are based on a known system model. The multiple model approach introduces uncertainties about the system model by a set of possible system models. In the multiple model approach for fixed models the true system does not change during the whole observation process, wheareas in the approach for ...
Brigitte Gundlich, Peter Teunissen
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