Results 201 to 210 of about 136,474,496 (243)

Neural Maximum-a-Posteriori Beamforming for Ultrasound Imaging

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023
Ultrasound imaging is an attractive imaging modality due to its low-cost and real-time feedback, although it often falls short in image quality compared to MRI and CT imaging. Conventional ultrasound image reconstruction, such as Delay-and-Sum beamforming, is derived from maximum-likelihood estimation.
Ben Luijten, Boudewine W. Ossenkoppele, Nico de Jong, Martin D. Verweij, Yonina C. Eldar, Massimo Mischi, Ruud J. G. van Sloun   +6 more
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Maximum a posteriori transduction

Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826), 2005
Transduction deals with the problem of estimating the values of a function at given points (called working samples) by a set of training samples. This paper proposes a maximum a posteriori (MAP) scheme for the transduction. The probability measure defined for the estimation is induced by the code length of the prediction error and the model with ...
null Li-Wei Wang, null Ju-Fu Feng
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Anomaly detection based on maximum a posteriori

Pattern Recognition Letters, 2018
Abstract In this paper, we propose a novel method to detect abnormal events from videos based on a maximum a posteriori (MAP). Conventional methods consider the events with low-probability with respect to a model of normal behavior as anomaly. Different from the traditional approaches, the anomaly detection is achieved by a MAP estimation in our ...
Shifeng Li, Chunxiao Liu, Yuqiang Yang
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Maximum a posteriori maximum entropy order determination

IEEE Transactions on Signal Processing, 1997
Summary: An instance crucial to most problems in signal processing is the selection of the order of a presupposed model. Examples are the determination of the putative number of signals present in white Gaussian noise or the number of noise-contaminated sources impinging on a passive sensor array.
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Maximum a posteriori estimation of signal rank

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
Estimating the number of linearly independent signals impinging on a collection of receivers is of increasing recent interest, particularly in connection with MIMO systems for communications and sensing. This paper derives maximum a posteriori estimators for signal rank from noisy data collected at multiple receivers. Situations in which noise variance
Songsri Sirianunpiboon, Stephen D. Howard, Douglas Cochran   +2 more
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Maximum a Posteriori Maximum Entropy Signal Denoising

AIP Conference Proceedings, 2007
When fitting wavelet based models, shrinkage of the empirical wavelet coefficients is an effective tool for signal denoising. Based on different approaches, different shrinkage functions have been proposed in the literature. The shrinkage functions derived using Bayesian estimation theory depend on the prior used on the wavelet coefficients.
Abd-Krim Seghouane, Luc Knockaert, Kevin H. Knuth, Ariel Caticha, Julian L. Center, Adom Giffin, Carlos C. Rodríguez   +6 more
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Maximum a posteriori height estimation in InSAR imaging

IEEE International Geoscience and Remote Sensing Symposium, 2003
A multi-frequencies maximum a posteriori (MAP) estimation of height profiles, from InSAR data, is presented in this paper. A quadratic MRF model is adopted to exploit a-priori information about the unknown image; the hyperparameter estimation, performed on a local basis, provides a powerful representation model for realistic height surfaces.
Giancarlo Ferraiuolo, Vito Pascazio, Gilda Schirinzi   +2 more
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A recursive maximum a posteriori estimator

Asian Journal of Control, 2010
Summary: Kalman Filter (KF) is the optimal state estimator for linear dynamical systems in the presence of zero mean white Gaussian noise. It is a Minimum Mean Square Error (MMSE) estimator. In the present work a Recursive Maximum A Posteriori Estimator (RMAPE) has been developed from basic principles of estimation.
Mukherjee, Arpita, Sengupta, Aparajita
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Maximum a posteriori array signal processing

ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005
A noncausal estimator is presented for the position versus time sequence of a source relative to an array of passive receivers. The position is estimated in terms of the bearing and range of the source which are modelled as second order Gauss-Markov discrete time sequences.
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Maximum a posteriori estimation approach to sparse recovery

2011 17th International Conference on Digital Signal Processing (DSP), 2011
We adopt a maximum a posteriori (MAP) estimation based approach for recovering sparse signals from a small number of measurements formed by computing the inner products of the signal with rows of a matrix. We assume that each component of the sparse signal is independent and identically distributed (i.i.d) random variable drawn from a Gaussian mixture ...
Md Mashud Hyder, Kaushik Mahata
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