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Moving object detection in framework of compressive sampling

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Journal of Systems Engineering and Electronics 2010年第5期21卷 740-745页

作者： Jing Li JunzhengWang Wei Shen Key Laboratory of Complex System Intelligent Control and Decision School of Automation Beijing Institute of Technology Beijing 100081 P. R. China

compressive sensing is a revolutionary idea proposed recently to achieve much lower sampling rate for *** the image application with limited resources the camera data can be stored and processed in compressed *** algorithm for moving object and region detection in video using a compressive sampling is *** algorithm estimates motion information of the moving object and regions in the video from the compressive measurements of the current image and background *** algorithm does not perform inverse compressive operation to obtain the actual pixels of the current image nor the estimated *** leads to a computationally efficient method and a system compared with the existing motion estimation *** experimental results show that the sampling rate can reduce to 25% without sacrificing performance.

关键词： compressive sampling compressive measurements moving object detection.

A METHOD OF IMAGE QUALITY ASSESSMENT FOR compressive sampling VIDEO TRANSMISSION

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Journal of Electronics(China) 2012年第6期29卷 598-603页

作者： Chen Shouning Zheng Baoyu Li Jing Institute of Signal Processing and Transmission Nanjing University of Posts and Telecommunications

Based on compressive sampling transmission model, we demonstrate here a method of quality evaluation for the reconstruction images, which is promising for the transmission of unstructured signal with reduced dimension. By this method, the auxiliary information of the recovery image quality is obtained as a feedback to control number of measurements from compressive sampling video stream. Therefore, the number of measurements can be easily derived at the condition of the absence of information sparsity, and the recovery image quality is effectively improved. Theoretical and experimental results show that this algorithm can estimate the quality of images effectively and is in well consistency with the traditional objective evaluation algorithm.

关键词： compressive sampling Image quality assessment Measurements feedback

Adaptive sampling strategy for characterizing spatial distribution of soil liquefaction potential using cone penetration test

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Journal of Rock Mechanics and Geotechnical Engineering 2022年第4期14卷 1221-1231页

作者： Zheng Guan Yu Wang Tengyuan Zhao Department of Architecture and Civil Engineering City University of Hong KongTat Chee AvenueKowloonHong KongChina School of Human Settlements and Civil Engineering Xi’an Jiaotong UniversityXi’an710049China

Characterizing spatial distribution of soil liquefaction potential is critical for assessing liquefactionrelated hazards(*** damages caused by liquefaction-induced differential settlement).However,in engineering practice,soil liquefaction potential is usually measured at limited locations in a specific site using in situ tests,*** penetration tests(CPTs),due to the restrictions of time,cost and access to subsurface *** these cases,liquefaction potential of soil at untested locations requires to be interpreted from limited measured data points using proper interpolation method,leading to remarkable statistical uncertainty in liquefaction *** underlines an important question of how to optimize the locations of CPT soundings and determine the minimum number of CPTs for achieving a target reliability level of liquefaction *** tackle this issue,this study proposes a smart sampling strategy for determining the minimum number of CPTs and their optimal locations in a selfadaptive and data-driven *** proposed sampling strategy leverages on information entropy and Bayesian compressive sampling(BCS).Both simulated and real CPT data are used to demonstrate the proposed *** examples indicate that the proposed method can adaptively and sequentially select the required number and optimal locations of CPTs.

关键词： Liquefaction potential Information entropy Cone penetration test(CPT) Site characterization compressive sampling

Stochastic Collocation on Unstructured Multivariate Meshes

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Communications in Computational Physics 2015年第6期18卷 1-36页

作者： Akil Narayan Tao Zhou Scientific Computing and Imaging Institute University of UtahSalt Lake CityUT 84112USA LSEC Institute of Computational Mathematics and Scientific/Engineering ComputingAMSSthe Chinese Academy of SciencesBeijingChina

Collocation has become a standard tool for approximation of parameterized systems in the uncertainty quantification(UQ)*** for leastsquares regularization,compressive sampling recovery,and interpolatory reconstruction are becoming standard tools used in a variety of *** of a collocation mesh is frequently a challenge,but methods that construct geometrically unstructured collocation meshes have shown great potential due to attractive theoretical properties and direct,simple generation and *** investigate properties of these meshes,presenting stability and accuracy results that can be used as guides for generating stochastic collocation grids in multiple dimensions.

关键词： Stochastic collocation unstructured methes least-squares compressive sampling least interpolation

2D sparse signal recovery via 2D orthogonal matching pursuit

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Science China(Information Sciences) 2012年第4期55卷 889-897页

作者： HUANG BorMin Space Science and Engineering Center University of Wisconsin-MadisonMadisonWI 53706USA

Recovery algorithms play a key role in compressive sampling (CS).Most of current CS recovery algo-rithms are originally designed for one-dimensional (1D) signal,while many practical signals are two-dimensional (2D).By utilizing 2D separable sampling,2D signal recovery problem can be converted into 1D signal recovery problem so that ordinary 1D recovery algorithms,*** matching pursuit (OMP),can be applied ***,even with 2D separable sampling,the memory usage and complexity at the decoder are still *** paper develops a novel recovery algorithm called 2D-OMP,which is an extension of *** the 2D-OMP,each atom in the dictionary is a *** each iteration,the decoder projects the sample matrix onto 2D atoms to select the best matched atom,and then renews the weights for all the already selected atoms via the least *** show that 2D-OMP is in fact equivalent to 1D-OMP,but it reduces recovery complexity and memory usage ***’s more important,by utilizing the same methodology used in this paper,one can even obtain higher dimensional OMP (say 3D-OMP,etc.) with ease.

关键词： compressive sampling 2D sparse signal recovery algorithm orthogonal matching pursuit

MR IMAGE RECONSTRUCTION BASED ON COMPREHENSIVE SPARSE PRIOR

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Journal of Electronics(China) 2012年第6期29卷 611-616页

作者： Ding Xinghao Chen Xianbo Huang Yue Mi Zengyuan School of Information Science and Technology Xiamen University

In this paper, a novel Magnetic Resonance (MR) reconstruction framework which combines image-wise and patch-wise sparse prior is proposed. For addressing, a truncated beta-Bernoulli process is firstly employed to enforce sparsity on overlapping image patches emphasizing local structures. Due to its properties, beta-Bernoulli process can adaptive infer the sparsity (number of non-zero coefficients) of each patch, an appropriate dictionary, and the noise variance simultaneously, which are prerequisite for iterative image reconstruction. Secondly, a General Gaussian Distribution (GGD) prior is introduced to engage image-wise sparsity for wavelet coefficients, which can be then estimated by a threshold denoising algorithm. Finally, MR image is reconstructed by patch-wise estimation, image-wise estimation and under-sampled k-space data with least square data fitting. Experimental results have demonstrated that proposed approach exhibits excellent reconstruction performance. Moreover, if the image is full of similar low-dimensional-structures, proposed algorithm has dramatically improved Peak Signal to Noise Ratio (PSNR) 7～9 dB, with comparisons to other state-of-art compressive sampling methods.

关键词： Image-wise sparse prior Patch-wise sparse prior Beta-Bernoulli process Low-dimensional-structure compressive sampling