[图书][B] Sparse representations and compressive sensing for imaging and vision

VM Patel, R Chellappa - 2013 - books.google.com
Compressed sensing or compressive sensing is a new concept in signal processing where
one measures a small number of non-adaptive linear combinations of the signal. These …

On the observability of linear systems from random, compressive measurements

MB Wakin, BM Sanandaji… - 49th IEEE Conference on …, 2010 - ieeexplore.ieee.org
Recovering or estimating the initial state of a high-dimensional system can require a
potentially large number of measurements. In this paper, we explain how this burden can be …

Efficient Bayesian tracking of multiple sources of neural activity: Algorithms and real-time FPGA implementation

L Miao, JJ Zhang, C Chakrabarti… - IEEE Transactions …, 2012 - ieeexplore.ieee.org
We propose new Bayesian algorithms to automatically track current dipole sources of neural
activity in real time. We integrate multiple particle filters to track the dynamic parameters of a …

Adaptive compressive sensing for target tracking within wireless visual sensor networks-based surveillance applications

S Fayed, S M. Youssef, A El-Helw, M Patwary… - Multimedia Tools and …, 2016 - Springer
Abstract Wireless Visual Sensor Networks (WVSNs) have gained significant importance in
the last few years and have emerged in several distinctive applications. The main aim is to …

Energy‐efficient compressive sensing for multi‐target tracking in wireless visual sensor networks

M Najimi, VS Sadeghi - International Journal of Communication …, 2022 - Wiley Online Library
Wireless visual sensor networks (WVSN) have vital roles in surveillance applications. In
these networks, wireless visual sensors include camera and transceiver module and collect …

Multiple imputations particle filters: convergence and performance analyses for nonlinear state estimation with missing data

XP Zhang, AS Khwaja, JA Luo… - IEEE Journal of …, 2015 - ieeexplore.ieee.org
In this paper, we present a multiple imputations particle filter (MIPF) to deal with non-linear
state estimation when part of the observations are missing. The MIPF uses randomly drawn …

Observability with random observations

BM Sanandaji, MB Wakin… - IEEE Transactions on …, 2014 - ieeexplore.ieee.org
Recovery of the initial state of a high-dimensional system can require a large number of
measurements. In this paper, we explain how this burden can be significantly reduced when …

[HTML][HTML] Convex feasibility modeling and projection methods for sparse signal recovery

A Carmi, Y Censor, P Gurfil - Journal of computational and applied …, 2012 - Elsevier
A computationally-efficient method for recovering sparse signals from a series of noisy
observations, known as the problem of compressed sensing (CS), is presented. The theory …

A coded aperture compressive imaging array and its visual detection and tracking algorithms for surveillance systems

J Chen, Y Wang, H Wu - Sensors, 2012 - mdpi.com
In this paper, we propose an application of a compressive imaging system to the problem of
wide-area video surveillance systems. A parallel coded aperture compressive imaging …

Nonlinear compressive particle filtering

H Ohlsson, M Verhaegen… - 52nd IEEE Conference on …, 2013 - ieeexplore.ieee.org
Many systems for which compressive sensing is used today are dynamical. The common
approach is to neglect the dynamics and see the problem as a sequence of independent …