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Product-of-Gaussian-mixture diffusion models for joint nonlinear MRI reconstruction. [PDF]
J Math Imaging VisNagler L, Zach M, Pock T.
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An interpretable deep concatenated architecture for osteoporosis detection using enhanced knee radiographs. [PDF]
Front Med (Lausanne)Kaur N +4 more
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Gaussian-Adaptive Bilateral Filter
IEEE Signal Processing Letters, 2020Recent studies have demonstrated that a bilateral filter can increase the quality of edge-preserving image smoothing significantly. Different strategies or mechanisms have been used to eliminate the brute-force computation in bilateral filters. However, blindly decreasing the processing time of the bilateral filter cannot further ameliorate the ...
Bo-Hao Chen
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Box Gaussian Mixture Filter $ $
IEEE Transactions on Automatic Control, 2010This note presents the box Gaussian mixture filter (BGMF), which is an efficient filter for the systems with mainly linear measurements but enables utilizing highly nonlinear measurements. BGMF contains a new way to approximate the prior distributions with a Gaussian mixture, whose components have small covariances.
Simo Ali-Loytty
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The Switching Hierarchical Gaussian Filter
2021 IEEE International Symposium on Information Theory (ISIT), 2021In this paper we discuss variational message passing-based (VMP) inference in a switching Hierarchical Gaussian Filter (HGF). An HGF is a flexible hierarchical state space model that supports closed-form VMP-based approximate inference for tracking of both states and slowly time-varying parameters. Since natural signals often submit to regime-switching
Ismail Senöz +4 more
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Proceedings of 13th International Conference on Pattern Recognition, 1996
A multiscale region detector for low-level image analysis is described. The basis of the detector is a set of filters similar to the Laplacian of an elliptical Gaussian. The responses of these filters to ideal ellipses are derived, and equations for determining the parameters of detected ellipses from the filter responses are found.
Scott A. Jackson, Narendra Ahuja
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A multiscale region detector for low-level image analysis is described. The basis of the detector is a set of filters similar to the Laplacian of an elliptical Gaussian. The responses of these filters to ideal ellipses are derived, and equations for determining the parameters of detected ellipses from the filter responses are found.
Scott A. Jackson, Narendra Ahuja
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Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing (Cat. No.01TH8563), 2002
Sequential Bayesian estimation for dynamic state space models involves recursive estimation of hidden states based on noisy observations. The update of filtering and predictive densities for nonlinear models with non-Gaussian noise using Monte Carlo particle filtering methods is considered.
Jayesh H. Kotecha, Petar M. Djuric
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Sequential Bayesian estimation for dynamic state space models involves recursive estimation of hidden states based on noisy observations. The update of filtering and predictive densities for nonlinear models with non-Gaussian noise using Monte Carlo particle filtering methods is considered.
Jayesh H. Kotecha, Petar M. Djuric
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2015
Modern databases tailored to highly distributed, fault tolerant management of information for big data applications exploit a classical data structure for reducing disk and network I/O as well as for managing data distribution: The Bloom filter. This data structure allows to encode small sets of elements, typically the keys in a key-value store, into a
Martin Werner 0001, Mirco Schönfeld
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Modern databases tailored to highly distributed, fault tolerant management of information for big data applications exploit a classical data structure for reducing disk and network I/O as well as for managing data distribution: The Bloom filter. This data structure allows to encode small sets of elements, typically the keys in a key-value store, into a
Martin Werner 0001, Mirco Schönfeld
openaire +1 more source