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reconstruction</title> <meta name="description" content="Search results for: sparse reconstruction"> <meta name="keywords" content="sparse reconstruction"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="sparse reconstruction"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 788</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: sparse reconstruction</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">788</span> Development of a Few-View Computed Tomographic Reconstruction Algorithm Using Multi-Directional Total Variation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chia%20Jui%20Hsieh">Chia Jui Hsieh</a>, <a href="https://publications.waset.org/abstracts/search?q=Jyh%20Cheng%20Chen"> Jyh Cheng Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Chih%20Wei%20Kuo"> Chih Wei Kuo</a>, <a href="https://publications.waset.org/abstracts/search?q=Ruei%20Teng%20Wang"> Ruei Teng Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Woei%20Chyn%20Chu"> Woei Chyn Chu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Compressed sensing (CS) based computed tomographic (CT) reconstruction algorithm utilizes total variation (TV) to transform CT image into sparse domain and minimizes L1-norm of sparse image for reconstruction. Different from the traditional CS based reconstruction which only calculates x-coordinate and y-coordinate TV to transform CT images into sparse domain, we propose a multi-directional TV to transform tomographic image into sparse domain for low-dose reconstruction. Our method considers all possible directions of TV calculations around a pixel, so the sparse transform for CS based reconstruction is more accurate. In 2D CT reconstruction, we use eight-directional TV to transform CT image into sparse domain. Furthermore, we also use 26-directional TV for 3D reconstruction. This multi-directional sparse transform method makes CS based reconstruction algorithm more powerful to reduce noise and increase image quality. To validate and evaluate the performance of this multi-directional sparse transform method, we use both Shepp-Logan phantom and a head phantom as the targets for reconstruction with the corresponding simulated sparse projection data (angular sampling interval is 5 deg and 6 deg, respectively). From the results, the multi-directional TV method can reconstruct images with relatively less artifacts compared with traditional CS based reconstruction algorithm which only calculates x-coordinate and y-coordinate TV. We also choose RMSE, PSNR, UQI to be the parameters for quantitative analysis. From the results of quantitative analysis, no matter which parameter is calculated, the multi-directional TV method, which we proposed, is better. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing%20%28CS%29" title="compressed sensing (CS)">compressed sensing (CS)</a>, <a href="https://publications.waset.org/abstracts/search?q=low-dose%20CT%20reconstruction" title=" low-dose CT reconstruction"> low-dose CT reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=total%20variation%20%28TV%29" title=" total variation (TV)"> total variation (TV)</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-directional%20gradient%20operator" title=" multi-directional gradient operator"> multi-directional gradient operator</a> </p> <a href="https://publications.waset.org/abstracts/77716/development-of-a-few-view-computed-tomographic-reconstruction-algorithm-using-multi-directional-total-variation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77716.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">256</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">787</span> Sparse-View CT Reconstruction Based on Nonconvex L1 − L2 Regularizations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Pour%20Yazdanpanah">Ali Pour Yazdanpanah</a>, <a href="https://publications.waset.org/abstracts/search?q=Farideh%20Foroozandeh%20Shahraki"> Farideh Foroozandeh Shahraki</a>, <a href="https://publications.waset.org/abstracts/search?q=Emma%20Regentova"> Emma Regentova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The reconstruction from sparse-view projections is one of important problems in computed tomography (CT) limited by the availability or feasibility of obtaining of a large number of projections. Traditionally, convex regularizers have been exploited to improve the reconstruction quality in sparse-view CT, and the convex constraint in those problems leads to an easy optimization process. However, convex regularizers often result in a biased approximation and inaccurate reconstruction in CT problems. Here, we present a nonconvex, Lipschitz continuous and non-smooth regularization model. The CT reconstruction is formulated as a nonconvex constrained L1 − L2 minimization problem and solved through a difference of convex algorithm and alternating direction of multiplier method which generates a better result than L0 or L1 regularizers in the CT reconstruction. We compare our method with previously reported high performance methods which use convex regularizers such as TV, wavelet, curvelet, and curvelet+TV (CTV) on the test phantom images. The results show that there are benefits in using the nonconvex regularizer in the sparse-view CT reconstruction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computed%20tomography" title="computed tomography">computed tomography</a>, <a href="https://publications.waset.org/abstracts/search?q=non-convex" title=" non-convex"> non-convex</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse-view%20reconstruction" title=" sparse-view reconstruction"> sparse-view reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=L1-L2%20minimization" title=" L1-L2 minimization"> L1-L2 minimization</a>, <a href="https://publications.waset.org/abstracts/search?q=difference%20of%20convex%20functions" title=" difference of convex functions"> difference of convex functions</a> </p> <a href="https://publications.waset.org/abstracts/70473/sparse-view-ct-reconstruction-based-on-nonconvex-l1-l2-regularizations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70473.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">316</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">786</span> Image Reconstruction Method Based on L0 Norm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jianhong%20Xiang">Jianhong Xiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hao%20Xiang"> Hao Xiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Linyu%20Wang"> Linyu Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Compressed sensing (CS) has a wide range of applications in sparse signal reconstruction. Aiming at the problems of low recovery accuracy and long reconstruction time of existing reconstruction algorithms in medical imaging, this paper proposes a corrected smoothing L0 algorithm based on compressed sensing (CSL0). First, an approximate hyperbolic tangent function (AHTF) that is more similar to the L0 norm is proposed to approximate the L0 norm. Secondly, in view of the "sawtooth phenomenon" in the steepest descent method and the problem of sensitivity to the initial value selection in the modified Newton method, the use of the steepest descent method and the modified Newton method are jointly optimized to improve the reconstruction accuracy. Finally, the CSL0 algorithm is simulated on various images. The results show that the algorithm proposed in this paper improves the reconstruction accuracy of the test image by 0-0. 98dB. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=smoothed%20L0" title="smoothed L0">smoothed L0</a>, <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing" title=" compressed sensing"> compressed sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20reconstruction" title=" sparse reconstruction"> sparse reconstruction</a> </p> <a href="https://publications.waset.org/abstracts/155598/image-reconstruction-method-based-on-l0-norm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155598.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">115</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">785</span> Compressed Sensing of Fetal Electrocardiogram Signals Based on Joint Block Multi-Orthogonal Least Squares Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiang%20Jianhong">Xiang Jianhong</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Cong"> Wang Cong</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Linyu"> Wang Linyu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the rise of medical IoT technologies, Wireless body area networks (WBANs) can collect fetal electrocardiogram (FECG) signals to support telemedicine analysis. The compressed sensing (CS)-based WBANs system can avoid the sampling of a large amount of redundant information and reduce the complexity and computing time of data processing, but the existing algorithms have poor signal compression and reconstruction performance. In this paper, a Joint block multi-orthogonal least squares (JBMOLS) algorithm is proposed. We apply the FECG signal to the Joint block sparse model (JBSM), and a comparative study of sparse transformation and measurement matrices is carried out. A FECG signal compression transmission mode based on Rbio5.5 wavelet, Bernoulli measurement matrix, and JBMOLS algorithm is proposed to improve the compression and reconstruction performance of FECG signal by CS-based WBANs. Experimental results show that the compression ratio (CR) required for accurate reconstruction of this transmission mode is increased by nearly 10%, and the runtime is saved by about 30%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=telemedicine" title="telemedicine">telemedicine</a>, <a href="https://publications.waset.org/abstracts/search?q=fetal%20ECG" title=" fetal ECG"> fetal ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing" title=" compressed sensing"> compressed sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=joint%20sparse%20reconstruction" title=" joint sparse reconstruction"> joint sparse reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=block%20sparse%20signal" title=" block sparse signal"> block sparse signal</a> </p> <a href="https://publications.waset.org/abstracts/154614/compressed-sensing-of-fetal-electrocardiogram-signals-based-on-joint-block-multi-orthogonal-least-squares-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154614.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">127</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">784</span> Sparsity Order Selection and Denoising in Compressed Sensing Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20Shamsi">Mahdi Shamsi</a>, <a href="https://publications.waset.org/abstracts/search?q=Tohid%20Yousefi%20Rezaii"> Tohid Yousefi Rezaii</a>, <a href="https://publications.waset.org/abstracts/search?q=Siavash%20Eftekharifar"> Siavash Eftekharifar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Compressed sensing (CS) is a new powerful mathematical theory concentrating on sparse signals which is widely used in signal processing. The main idea is to sense sparse signals by far fewer measurements than the Nyquist sampling rate, but the reconstruction process becomes nonlinear and more complicated. Common dilemma in sparse signal recovery in CS is the lack of knowledge about sparsity order of the signal, which can be viewed as model order selection procedure. In this paper, we address the problem of sparsity order estimation in sparse signal recovery. This is of main interest in situations where the signal sparsity is unknown or the signal to be recovered is approximately sparse. It is shown that the proposed method also leads to some kind of signal denoising, where the observations are contaminated with noise. Finally, the performance of the proposed approach is evaluated in different scenarios and compared to an existing method, which shows the effectiveness of the proposed method in terms of order selection as well as denoising. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing" title="compressed sensing">compressed sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20denoising" title=" data denoising"> data denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20order%20selection" title=" model order selection"> model order selection</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20representation" title=" sparse representation"> sparse representation</a> </p> <a href="https://publications.waset.org/abstracts/31470/sparsity-order-selection-and-denoising-in-compressed-sensing-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31470.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">483</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">783</span> A New Framework for ECG Signal Modeling and Compression Based on Compressed Sensing Theory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Siavash%20Eftekharifar">Siavash Eftekharifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Tohid%20Yousefi%20Rezaii"> Tohid Yousefi Rezaii</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20Shamsi"> Mahdi Shamsi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this paper is to exploit compressed sensing (CS) method in order to model and compress the electrocardiogram (ECG) signals at a high compression ratio. In order to obtain a sparse representation of the ECG signals, first a suitable basis matrix with Gaussian kernels, which are shown to nicely fit the ECG signals, is constructed. Then the sparse model is extracted by applying some optimization technique. Finally, the CS theory is utilized to obtain a compressed version of the sparse signal. Reconstruction of the ECG signal from the compressed version is also done to prove the reliability of the algorithm. At this stage, a greedy optimization technique is used to reconstruct the ECG signal and the Mean Square Error (MSE) is calculated to evaluate the precision of the proposed compression method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing" title="compressed sensing">compressed sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG%20compression" title=" ECG compression"> ECG compression</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20kernel" title=" Gaussian kernel"> Gaussian kernel</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20representation" title=" sparse representation"> sparse representation</a> </p> <a href="https://publications.waset.org/abstracts/31469/a-new-framework-for-ecg-signal-modeling-and-compression-based-on-compressed-sensing-theory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31469.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">462</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">782</span> Off-Grid Sparse Inverse Synthetic Aperture Imaging by Basis Shift Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mengjun%20Yang">Mengjun Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhulin%20Zong"> Zhulin Zong</a>, <a href="https://publications.waset.org/abstracts/search?q=Jie%20Gao"> Jie Gao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a new and robust algorithm is proposed to achieve high resolution for inverse synthetic aperture radar (ISAR) imaging in the compressive sensing (CS) framework. Traditional CS based methods have to assume that unknown scatters exactly lie on the pre-divided grids; otherwise, their reconstruction performance dropped significantly. In this processing algorithm, several basis shifts are utilized to achieve the same effect as grid refinement does. The detailed implementation of the basis shift algorithm is presented in this paper. From the simulation we can see that using the basis shift algorithm, imaging precision can be improved. The effectiveness and feasibility of the proposed method are investigated by the simulation results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ISAR%20imaging" title="ISAR imaging">ISAR imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20reconstruction" title=" sparse reconstruction"> sparse reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=off-grid" title=" off-grid"> off-grid</a>, <a href="https://publications.waset.org/abstracts/search?q=basis%20shift" title=" basis shift"> basis shift</a> </p> <a href="https://publications.waset.org/abstracts/90250/off-grid-sparse-inverse-synthetic-aperture-imaging-by-basis-shift-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90250.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">265</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">781</span> Evaluation of Fusion Sonar and Stereo Camera System for 3D Reconstruction of Underwater Archaeological Object</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yadpiroon%20Onmek">Yadpiroon Onmek</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean%20Triboulet"> Jean Triboulet</a>, <a href="https://publications.waset.org/abstracts/search?q=Sebastien%20Druon"> Sebastien Druon</a>, <a href="https://publications.waset.org/abstracts/search?q=Bruno%20Jouvencel"> Bruno Jouvencel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The objective of this paper is to develop the 3D underwater reconstruction of archaeology object, which is based on the fusion between a sonar system and stereo camera system. The underwater images are obtained from a calibrated camera system. The multiples image pairs are input, and we first solve the problem of image processing by applying the well-known filter, therefore to improve the quality of underwater images. The features of interest between image pairs are selected by well-known methods: a FAST detector and FLANN descriptor. Subsequently, the RANSAC method is applied to reject outlier points. The putative inliers are matched by triangulation to produce the local sparse point clouds in 3D space, using a pinhole camera model and Euclidean distance estimation. The SFM technique is used to carry out the global sparse point clouds. Finally, the ICP method is used to fusion the sonar information with the stereo model. The final 3D models have a précised by measurement comparing with the real object. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3D%20reconstruction" title="3D reconstruction">3D reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=archaeology" title=" archaeology"> archaeology</a>, <a href="https://publications.waset.org/abstracts/search?q=fusion" title=" fusion"> fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=stereo%20system" title=" stereo system"> stereo system</a>, <a href="https://publications.waset.org/abstracts/search?q=sonar%20system" title=" sonar system"> sonar system</a>, <a href="https://publications.waset.org/abstracts/search?q=underwater" title=" underwater"> underwater</a> </p> <a href="https://publications.waset.org/abstracts/73700/evaluation-of-fusion-sonar-and-stereo-camera-system-for-3d-reconstruction-of-underwater-archaeological-object" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73700.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">299</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">780</span> End-to-End Pyramid Based Method for Magnetic Resonance Imaging Reconstruction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omer%20Cahana">Omer Cahana</a>, <a href="https://publications.waset.org/abstracts/search?q=Ofer%20Levi"> Ofer Levi</a>, <a href="https://publications.waset.org/abstracts/search?q=Maya%20Herman"> Maya Herman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Magnetic Resonance Imaging (MRI) is a lengthy medical scan that stems from a long acquisition time. Its length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach such as Compress Sensing (CS) or Parallel Imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. To achieve that, two conditions must be satisfied: i) the signal must be sparse under a known transform domain, and ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm must be applied to recover the signal. While the rapid advances in Deep Learning (DL) have had tremendous successes in various computer vision tasks, the field of MRI reconstruction is still in its early stages. In this paper, we present an end-to-end method for MRI reconstruction from k-space to image. Our method contains two parts. The first is sensitivity map estimation (SME), which is a small yet effective network that can easily be extended to a variable number of coils. The second is reconstruction, which is a top-down architecture with lateral connections developed for building high-level refinement at all scales. Our method holds the state-of-art fastMRI benchmark, which is the largest, most diverse benchmark for MRI reconstruction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20imaging" title="magnetic resonance imaging">magnetic resonance imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20reconstruction" title=" image reconstruction"> image reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=pyramid%20network" title=" pyramid network"> pyramid network</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/150838/end-to-end-pyramid-based-method-for-magnetic-resonance-imaging-reconstruction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150838.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">91</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">779</span> Non-Local Simultaneous Sparse Unmixing for Hyperspectral Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fanqiang%20Kong">Fanqiang Kong</a>, <a href="https://publications.waset.org/abstracts/search?q=Chending%20Bian"> Chending Bian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sparse unmixing is a promising approach in a semisupervised fashion by assuming that the observed pixels of a hyperspectral image can be expressed in the form of linear combination of only a few pure spectral signatures (end members) in an available spectral library. However, the sparse unmixing problem still remains a great challenge at finding the optimal subset of endmembers for the observed data from a large standard spectral library, without considering the spatial information. Under such circumstances, a sparse unmixing algorithm termed as non-local simultaneous sparse unmixing (NLSSU) is presented. In NLSSU, the non-local simultaneous sparse representation method for endmember selection of sparse unmixing, is used to finding the optimal subset of endmembers for the similar image patch set in the hyperspectral image. And then, the non-local means method, as a regularizer for abundance estimation of sparse unmixing, is used to exploit the abundance image non-local self-similarity. Experimental results on both simulated and real data demonstrate that NLSSU outperforms the other algorithms, with a better spectral unmixing accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hyperspectral%20unmixing" title="hyperspectral unmixing">hyperspectral unmixing</a>, <a href="https://publications.waset.org/abstracts/search?q=simultaneous%20sparse%20representation" title=" simultaneous sparse representation"> simultaneous sparse representation</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20regression" title=" sparse regression"> sparse regression</a>, <a href="https://publications.waset.org/abstracts/search?q=non-local%20means" title=" non-local means"> non-local means</a> </p> <a href="https://publications.waset.org/abstracts/71689/non-local-simultaneous-sparse-unmixing-for-hyperspectral-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71689.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">245</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">778</span> HR MRI CS Based Image Reconstruction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Krzysztof%20Malczewski">Krzysztof Malczewski</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Magnetic Resonance Imaging (MRI) reconstruction algorithm using compressed sensing is presented in this paper. It is exhibited that the offered approach improves MR images spatial resolution in circumstances when highly undersampled k-space trajectories are applied. Compressed Sensing (CS) aims at signal and images reconstructing from significantly fewer measurements than were conventionally assumed necessary. Magnetic Resonance Imaging (MRI) is a fundamental medical imaging method struggles with an inherently slow data acquisition process. The use of CS to MRI has the potential for significant scan time reductions, with visible benefits for patients and health care economics. In this study the objective is to combine super-resolution image enhancement algorithm with CS framework benefits to achieve high resolution MR output image. Both methods emphasize on maximizing image sparsity on known sparse transform domain and minimizing fidelity. The presented algorithm considers the cardiac and respiratory movements. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=super-resolution" title="super-resolution">super-resolution</a>, <a href="https://publications.waset.org/abstracts/search?q=MRI" title=" MRI"> MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing" title=" compressed sensing"> compressed sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse-sense" title=" sparse-sense"> sparse-sense</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20enhancement" title=" image enhancement"> image enhancement</a> </p> <a href="https://publications.waset.org/abstracts/6021/hr-mri-cs-based-image-reconstruction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6021.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">430</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">777</span> An Improved Method to Compute Sparse Graphs for Traveling Salesman Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Y.%20Wang">Y. Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Traveling salesman problem (TSP) is NP-hard in combinatorial optimization. The research shows the algorithms for TSP on the sparse graphs have the shorter computation time than those for TSP according to the complete graphs. We present an improved iterative algorithm to compute the sparse graphs for TSP by frequency graphs computed with frequency quadrilaterals. The iterative algorithm is enhanced by adjusting two parameters of the algorithm. The computation time of the algorithm is <em>O</em>(<em>CN</em><sub>max</sub><em>n</em><sup>2</sup>) where <em>C</em> is the iterations, <em>N</em><sub>max</sub> is the maximum number of frequency quadrilaterals containing each edge and <em>n</em> is the scale of TSP. The experimental results showed the computed sparse graphs generally have less than 5<em>n</em> edges for most of these Euclidean instances. Moreover, the maximum degree and minimum degree of the vertices in the sparse graphs do not have much difference. Thus, the computation time of the methods to resolve the TSP on these sparse graphs will be greatly reduced. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=frequency%20quadrilateral" title="frequency quadrilateral">frequency quadrilateral</a>, <a href="https://publications.waset.org/abstracts/search?q=iterative%20algorithm" title=" iterative algorithm"> iterative algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20graph" title=" sparse graph"> sparse graph</a>, <a href="https://publications.waset.org/abstracts/search?q=traveling%20salesman%20problem" title=" traveling salesman problem"> traveling salesman problem</a> </p> <a href="https://publications.waset.org/abstracts/82737/an-improved-method-to-compute-sparse-graphs-for-traveling-salesman-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82737.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">233</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">776</span> Efects of Data Corelation in a Sparse-View Compresive Sensing Based Image Reconstruction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sajid%20Abas">Sajid Abas</a>, <a href="https://publications.waset.org/abstracts/search?q=Jon%20Pyo%20Hong"> Jon Pyo Hong</a>, <a href="https://publications.waset.org/abstracts/search?q=Jung-Ryun%20Le"> Jung-Ryun Le</a>, <a href="https://publications.waset.org/abstracts/search?q=Seungryong%20Cho"> Seungryong Cho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Computed tomography and laminography are heavily investigated in a compressive sensing based image reconstruction framework to reduce the dose to the patients as well as to the radiosensitive devices such as multilayer microelectronic circuit boards. Nowadays researchers are actively working on optimizing the compressive sensing based iterative image reconstruction algorithm to obtain better quality images. However, the effects of the sampled data’s properties on reconstructed the image’s quality, particularly in an insufficient sampled data conditions have not been explored in computed laminography. In this paper, we investigated the effects of two data properties i.e. sampling density and data incoherence on the reconstructed image obtained by conventional computed laminography and a recently proposed method called spherical sinusoidal scanning scheme. We have found that in a compressive sensing based image reconstruction framework, the image quality mainly depends upon the data incoherence when the data is uniformly sampled. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computed%20tomography" title="computed tomography">computed tomography</a>, <a href="https://publications.waset.org/abstracts/search?q=computed%20laminography" title=" computed laminography"> computed laminography</a>, <a href="https://publications.waset.org/abstracts/search?q=compressive%20sending" title=" compressive sending"> compressive sending</a>, <a href="https://publications.waset.org/abstracts/search?q=low-dose" title=" low-dose"> low-dose</a> </p> <a href="https://publications.waset.org/abstracts/13025/efects-of-data-corelation-in-a-sparse-view-compresive-sensing-based-image-reconstruction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13025.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">464</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">775</span> Sparse Principal Component Analysis: A Least Squares Approximation Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Giovanni%20Merola">Giovanni Merola</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sparse Principal Components Analysis aims to find principal components with few non-zero loadings. We derive such sparse solutions by adding a genuine sparsity requirement to the original Principal Components Analysis (PCA) objective function. This approach differs from others because it preserves PCA's original optimality: uncorrelatedness of the components and least squares approximation of the data. To identify the best subset of non-zero loadings we propose a branch-and-bound search and an iterative elimination algorithm. This last algorithm finds sparse solutions with large loadings and can be run without specifying the cardinality of the loadings and the number of components to compute in advance. We give thorough comparisons with the existing sparse PCA methods and several examples on real datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SPCA" title="SPCA">SPCA</a>, <a href="https://publications.waset.org/abstracts/search?q=uncorrelated%20components" title=" uncorrelated components"> uncorrelated components</a>, <a href="https://publications.waset.org/abstracts/search?q=branch-and-bound" title=" branch-and-bound"> branch-and-bound</a>, <a href="https://publications.waset.org/abstracts/search?q=backward%20elimination" title=" backward elimination"> backward elimination</a> </p> <a href="https://publications.waset.org/abstracts/14630/sparse-principal-component-analysis-a-least-squares-approximation-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14630.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">381</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">774</span> A Picture is worth a Billion Bits: Real-Time Image Reconstruction from Dense Binary Pixels</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tal%20Remez">Tal Remez</a>, <a href="https://publications.waset.org/abstracts/search?q=Or%20Litany"> Or Litany</a>, <a href="https://publications.waset.org/abstracts/search?q=Alex%20Bronstein"> Alex Bronstein</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The pursuit of smaller pixel sizes at ever increasing resolution in digital image sensors is mainly driven by the stringent price and form-factor requirements of sensors and optics in the cellular phone market. Recently, Eric Fossum proposed a novel concept of an image sensor with dense sub-diffraction limit one-bit pixels (jots), which can be considered a digital emulation of silver halide photographic film. This idea has been recently embodied as the EPFL Gigavision camera. A major bottleneck in the design of such sensors is the image reconstruction process, producing a continuous high dynamic range image from oversampled binary measurements. The extreme quantization of the Poisson statistics is incompatible with the assumptions of most standard image processing and enhancement frameworks. The recently proposed maximum-likelihood (ML) approach addresses this difficulty, but suffers from image artifacts and has impractically high computational complexity. In this work, we study a variant of a sensor with binary threshold pixels and propose a reconstruction algorithm combining an ML data fitting term with a sparse synthesis prior. We also show an efficient hardware-friendly real-time approximation of this inverse operator. Promising results are shown on synthetic data as well as on HDR data emulated using multiple exposures of a regular CMOS sensor. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binary%20pixels" title="binary pixels">binary pixels</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood" title=" maximum likelihood"> maximum likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20coding" title=" sparse coding"> sparse coding</a> </p> <a href="https://publications.waset.org/abstracts/38705/a-picture-is-worth-a-billion-bits-real-time-image-reconstruction-from-dense-binary-pixels" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38705.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">201</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">773</span> F-VarNet: Fast Variational Network for MRI Reconstruction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omer%20Cahana">Omer Cahana</a>, <a href="https://publications.waset.org/abstracts/search?q=Maya%20Herman"> Maya Herman</a>, <a href="https://publications.waset.org/abstracts/search?q=Ofer%20Levi"> Ofer Levi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Magnetic resonance imaging (MRI) is a long medical scan that stems from a long acquisition time. This length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach, such as compress sensing (CS) or parallel imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. In order to achieve that, two properties have to exist: i) the signal must be sparse under a known transform domain, ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm needs to be applied to recover the signal. While the rapid advance in the deep learning (DL) field, which has demonstrated tremendous successes in various computer vision task’s, the field of MRI reconstruction is still in an early stage. In this paper, we present an extension of the state-of-the-art model in MRI reconstruction -VarNet. We utilize VarNet by using dilated convolution in different scales, which extends the receptive field to capture more contextual information. Moreover, we simplified the sensitivity map estimation (SME), for it holds many unnecessary layers for this task. Those improvements have shown significant decreases in computation costs as well as higher accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MRI" title="MRI">MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=variational%20network" title=" variational network"> variational network</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=compress%20sensing" title=" compress sensing"> compress sensing</a> </p> <a href="https://publications.waset.org/abstracts/145519/f-varnet-fast-variational-network-for-mri-reconstruction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145519.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">161</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">772</span> Performance Analysis and Optimization for Diagonal Sparse Matrix-Vector Multiplication on Machine Learning Unit</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qiuyu%20Dai">Qiuyu Dai</a>, <a href="https://publications.waset.org/abstracts/search?q=Haochong%20Zhang"> Haochong Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiangrong%20Liu"> Xiangrong Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Diagonal sparse matrix-vector multiplication is a well-studied topic in the fields of scientific computing and big data processing. However, when diagonal sparse matrices are stored in DIA format, there can be a significant number of padded zero elements and scattered points, which can lead to a degradation in the performance of the current DIA kernel. This can also lead to excessive consumption of computational and memory resources. In order to address these issues, the authors propose the DIA-Adaptive scheme and its kernel, which leverages the parallel instruction sets on MLU. The researchers analyze the effect of allocating a varying number of threads, clusters, and hardware architectures on the performance of SpMV using different formats. The experimental results indicate that the proposed DIA-Adaptive scheme performs well and offers excellent parallelism. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20method" title="adaptive method">adaptive method</a>, <a href="https://publications.waset.org/abstracts/search?q=DIA" title=" DIA"> DIA</a>, <a href="https://publications.waset.org/abstracts/search?q=diagonal%20sparse%20matrices" title=" diagonal sparse matrices"> diagonal sparse matrices</a>, <a href="https://publications.waset.org/abstracts/search?q=MLU" title=" MLU"> MLU</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20matrix-vector%20multiplication" title=" sparse matrix-vector multiplication"> sparse matrix-vector multiplication</a> </p> <a href="https://publications.waset.org/abstracts/161003/performance-analysis-and-optimization-for-diagonal-sparse-matrix-vector-multiplication-on-machine-learning-unit" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161003.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">134</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">771</span> A Transform Domain Function Controlled VSSLMS Algorithm for Sparse System Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cemil%20Turan">Cemil Turan</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Shukri%20Salman"> Mohammad Shukri Salman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The convergence rate of the least-mean-square (LMS) algorithm deteriorates if the input signal to the filter is correlated. In a system identification problem, this convergence rate can be improved if the signal is white and/or if the system is sparse. We recently proposed a sparse transform domain LMS-type algorithm that uses a variable step-size for a sparse system identification. The proposed algorithm provided high performance even if the input signal is highly correlated. In this work, we investigate the performance of the proposed TD-LMS algorithm for a large number of filter tap which is also a critical issue for standard LMS algorithm. Additionally, the optimum value of the most important parameter is calculated for all experiments. Moreover, the convergence analysis of the proposed algorithm is provided. The performance of the proposed algorithm has been compared to different algorithms in a sparse system identification setting of different sparsity levels and different number of filter taps. Simulations have shown that the proposed algorithm has prominent performance compared to the other algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20filtering" title="adaptive filtering">adaptive filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20system%20identification" title=" sparse system identification"> sparse system identification</a>, <a href="https://publications.waset.org/abstracts/search?q=TD-LMS%20algorithm" title=" TD-LMS algorithm"> TD-LMS algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=VSSLMS%20algorithm" title=" VSSLMS algorithm"> VSSLMS algorithm</a> </p> <a href="https://publications.waset.org/abstracts/72335/a-transform-domain-function-controlled-vsslms-algorithm-for-sparse-system-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72335.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">360</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">770</span> Channel Estimation Using Deep Learning for Reconfigurable Intelligent Surfaces-Assisted Millimeter Wave Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ting%20Gao">Ting Gao</a>, <a href="https://publications.waset.org/abstracts/search?q=Mingyue%20He"> Mingyue He</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Reconfigurable intelligent surfaces (RISs) are expected to be an important part of next-generation wireless communication networks due to their potential to reduce the hardware cost and energy consumption of millimeter Wave (mmWave) massive multiple-input multiple-output (MIMO) technology. However, owing to the lack of signal processing abilities of the RIS, the perfect channel state information (CSI) in RIS-assisted communication systems is difficult to acquire. In this paper, the uplink channel estimation for mmWave systems with a hybrid active/passive RIS architecture is studied. Specifically, a deep learning-based estimation scheme is proposed to estimate the channel between the RIS and the user. In particular, the sparse structure of the mmWave channel is exploited to formulate the channel estimation as a sparse reconstruction problem. To this end, the proposed approach is derived to obtain the distribution of non-zero entries in a sparse channel. After that, the channel is reconstructed by utilizing the least-squares (LS) algorithm and compressed sensing (CS) theory. The simulation results demonstrate that the proposed channel estimation scheme is superior to existing solutions even in low signal-to-noise ratio (SNR) environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=channel%20estimation" title="channel estimation">channel estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=reconfigurable%20intelligent%20surface" title=" reconfigurable intelligent surface"> reconfigurable intelligent surface</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20communication" title=" wireless communication"> wireless communication</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/148896/channel-estimation-using-deep-learning-for-reconfigurable-intelligent-surfaces-assisted-millimeter-wave-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148896.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">150</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">769</span> KSVD-SVM Approach for Spontaneous Facial Expression Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dawood%20Al%20Chanti">Dawood Al Chanti</a>, <a href="https://publications.waset.org/abstracts/search?q=Alice%20Caplier"> Alice Caplier</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sparse representations of signals have received a great deal of attention in recent years. In this paper, the interest of using sparse representation as a mean for performing sparse discriminative analysis between spontaneous facial expressions is demonstrated. An automatic facial expressions recognition system is presented. It uses a KSVD-SVM approach which is made of three main stages: A pre-processing and feature extraction stage, which solves the problem of shared subspace distribution based on the random projection theory, to obtain low dimensional discriminative and reconstructive features; A dictionary learning and sparse coding stage, which uses the KSVD model to learn discriminative under or over dictionaries for sparse coding; Finally a classification stage, which uses a SVM classifier for facial expressions recognition. Our main concern is to be able to recognize non-basic affective states and non-acted expressions. Extensive experiments on the JAFFE static acted facial expressions database but also on the DynEmo dynamic spontaneous facial expressions database exhibit very good recognition rates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dictionary%20learning" title="dictionary learning">dictionary learning</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20projection" title=" random projection"> random projection</a>, <a href="https://publications.waset.org/abstracts/search?q=pose%20and%20spontaneous%20facial%20expression" title=" pose and spontaneous facial expression"> pose and spontaneous facial expression</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20representation" title=" sparse representation"> sparse representation</a> </p> <a href="https://publications.waset.org/abstracts/51683/ksvd-svm-approach-for-spontaneous-facial-expression-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51683.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">305</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">768</span> Parametric Template-Based 3D Reconstruction of the Human Body</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiahe%20Liu">Jiahe Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongyang%20Yu"> Hongyang Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Feng%20Qian"> Feng Qian</a>, <a href="https://publications.waset.org/abstracts/search?q=Miao%20Luo"> Miao Luo</a>, <a href="https://publications.waset.org/abstracts/search?q=Linhang%20Zhu"> Linhang Zhu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study proposed a 3D human body reconstruction method, which integrates multi-view joint information into a set of joints and processes it with a parametric human body template. Firstly, we obtained human body image information captured from multiple perspectives. The multi-view information can avoid self-occlusion and occlusion problems during the reconstruction process. Then, we used the MvP algorithm to integrate multi-view joint information into a set of joints. Next, we used the parametric human body template SMPL-X to obtain more accurate three-dimensional human body reconstruction results. Compared with the traditional single-view parametric human body template reconstruction, this method significantly improved the accuracy and stability of the reconstruction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=parametric%20human%20body%20templates" title="parametric human body templates">parametric human body templates</a>, <a href="https://publications.waset.org/abstracts/search?q=reconstruction%20of%20the%20human%20body" title=" reconstruction of the human body"> reconstruction of the human body</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-view" title=" multi-view"> multi-view</a>, <a href="https://publications.waset.org/abstracts/search?q=joint" title=" joint"> joint</a> </p> <a href="https://publications.waset.org/abstracts/173775/parametric-template-based-3d-reconstruction-of-the-human-body" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173775.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">79</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">767</span> Synthetic Dermal Template Use in the Reconstruction of a Chronic Scalp Wound</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Stephanie%20Cornish">Stephanie Cornish</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of synthetic dermal templates, also known as dermal matrices, such as PolyNovo® Biodegradable Temporising Matrix (BTM), has been well established in the reconstruction of acute wounds with a full thickness defect of the skin. Its use has become common place in the treatment of full thickness burns and is not unfamiliar in the realm of necrotising fasciitis, free flap donor site reconstruction, and the management of acute traumatic wounds. However, the use of dermal templates for more chronic wounds is rare. The authors present the successful use of BTM in the reconstruction of a chronic scalp wound following the excision of a malignancy and multiple previous failed attempts at repair, thus demonstrating the potential for an increased scope of use. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dermal%20template" title="dermal template">dermal template</a>, <a href="https://publications.waset.org/abstracts/search?q=BTM" title=" BTM"> BTM</a>, <a href="https://publications.waset.org/abstracts/search?q=chronic" title=" chronic"> chronic</a>, <a href="https://publications.waset.org/abstracts/search?q=scalp%20wound" title=" scalp wound"> scalp wound</a>, <a href="https://publications.waset.org/abstracts/search?q=reconstruction" title=" reconstruction"> reconstruction</a> </p> <a href="https://publications.waset.org/abstracts/152147/synthetic-dermal-template-use-in-the-reconstruction-of-a-chronic-scalp-wound" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152147.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">91</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">766</span> Social Capital in Housing Reconstruction Post Disaster Case of Yogyakarta Post Earthquake</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ikaputra">Ikaputra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper will focus on the concept of social capital for especially housing reconstruction Post Disaster. The context of the study is Indonesia and Yogyakarta Post Earthquake 2006 as a case, but it is expected that the concept can be adopted in general post disaster reconstruction. The discussion will begin by addressing issues on House Reconstruction Post Disaster in Indonesia and Yogyakarta; defining Social Capital as a concept for effective management capacity based on community; Social Capital Post Java Earthquake utilizing <em>Gotong Royong</em>—community mutual self-help, and Approach and Strategy towards Community-based Reconstruction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=community%20empowerment" title="community empowerment">community empowerment</a>, <a href="https://publications.waset.org/abstracts/search?q=Gotong%20Royong" title=" Gotong Royong"> Gotong Royong</a>, <a href="https://publications.waset.org/abstracts/search?q=post%20disaster" title=" post disaster"> post disaster</a>, <a href="https://publications.waset.org/abstracts/search?q=reconstruction" title=" reconstruction"> reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20capital" title=" social capital"> social capital</a>, <a href="https://publications.waset.org/abstracts/search?q=Yogyakarta-Indonesia" title=" Yogyakarta-Indonesia"> Yogyakarta-Indonesia</a> </p> <a href="https://publications.waset.org/abstracts/73027/social-capital-in-housing-reconstruction-post-disaster-case-of-yogyakarta-post-earthquake" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73027.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">325</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">765</span> Operative Tips of Strattice Based Breast Reconstruction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cho%20Ee%20Ng">Cho Ee Ng</a>, <a href="https://publications.waset.org/abstracts/search?q=Hazem%20Khout"> Hazem Khout</a>, <a href="https://publications.waset.org/abstracts/search?q=Tarannum%20Fasih"> Tarannum Fasih</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Acellular dermal matrices are increasingly used to reinforce the lower pole of the breast during implant breast reconstruction. There is no standard technique described in literature for the use of this product. In this article, we share our operative method of fixation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=strattice" title="strattice">strattice</a>, <a href="https://publications.waset.org/abstracts/search?q=acellular%20dermal%20matric" title=" acellular dermal matric"> acellular dermal matric</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20reconstruction" title=" breast reconstruction"> breast reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=implant" title=" implant"> implant</a> </p> <a href="https://publications.waset.org/abstracts/24838/operative-tips-of-strattice-based-breast-reconstruction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24838.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">396</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">764</span> 3D Reconstruction of Human Body Based on Gender Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiahe%20Liu">Jiahe Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongyang%20Yu"> Hongyang Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Feng%20Qian"> Feng Qian</a>, <a href="https://publications.waset.org/abstracts/search?q=Miao%20Luo"> Miao Luo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> SMPL-X was a powerful parametric human body model that included male, neutral, and female models, with significant gender differences between these three models. During the process of 3D human body reconstruction, the correct selection of standard templates was crucial for obtaining accurate results. To address this issue, we developed an efficient gender classification algorithm to automatically select the appropriate template for 3D human body reconstruction. The key to this gender classification algorithm was the precise analysis of human body features. By using the SMPL-X model, the algorithm could detect and identify gender features of the human body, thereby determining which standard template should be used. The accuracy of this algorithm made the 3D reconstruction process more accurate and reliable, as it could adjust model parameters based on individual gender differences. SMPL-X and the related gender classification algorithm have brought important advancements to the field of 3D human body reconstruction. By accurately selecting standard templates, they have improved the accuracy of reconstruction and have broad potential in various application fields. These technologies continue to drive the development of the 3D reconstruction field, providing us with more realistic and accurate human body models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gender%20classification" title="gender classification">gender classification</a>, <a href="https://publications.waset.org/abstracts/search?q=joint%20detection" title=" joint detection"> joint detection</a>, <a href="https://publications.waset.org/abstracts/search?q=SMPL-X" title=" SMPL-X"> SMPL-X</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20reconstruction" title=" 3D reconstruction"> 3D reconstruction</a> </p> <a href="https://publications.waset.org/abstracts/173842/3d-reconstruction-of-human-body-based-on-gender-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173842.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">70</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">763</span> Partially Knowing of Least Support Orthogonal Matching Pursuit (PKLS-OMP) for Recovering Signal</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Israa%20Sh.%20Tawfic">Israa Sh. Tawfic</a>, <a href="https://publications.waset.org/abstracts/search?q=Sema%20Koc%20Kayhan"> Sema Koc Kayhan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Given a large sparse signal, great wishes are to reconstruct the signal precisely and accurately from lease number of measurements as possible as it could. Although this seems possible by theory, the difficulty is in built an algorithm to perform the accuracy and efficiency of reconstructing. This paper proposes a new proved method to reconstruct sparse signal depend on using new method called Least Support Matching Pursuit (LS-OMP) merge it with the theory of Partial Knowing Support (PSK) given new method called Partially Knowing of Least Support Orthogonal Matching Pursuit (PKLS-OMP). The new methods depend on the greedy algorithm to compute the support which depends on the number of iterations. So to make it faster, the PKLS-OMP adds the idea of partial knowing support of its algorithm. It shows the efficiency, simplicity, and accuracy to get back the original signal if the sampling matrix satisfies the Restricted Isometry Property (RIP). Simulation results also show that it outperforms many algorithms especially for compressible signals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing" title="compressed sensing">compressed sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=lest%20support%20orthogonal%20matching%20pursuit" title=" lest support orthogonal matching pursuit"> lest support orthogonal matching pursuit</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20knowing%20support" title=" partial knowing support"> partial knowing support</a>, <a href="https://publications.waset.org/abstracts/search?q=restricted%20isometry%20property" title=" restricted isometry property"> restricted isometry property</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20reconstruction" title=" signal reconstruction"> signal reconstruction</a> </p> <a href="https://publications.waset.org/abstracts/16008/partially-knowing-of-least-support-orthogonal-matching-pursuit-pkls-omp-for-recovering-signal" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16008.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">240</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">762</span> The Impact of COVID-19 on Reconstructive Breast Surgery and Future Prospective</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amenah%20Galo">Amenah Galo</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Farid"> Mohammed Farid</a>, <a href="https://publications.waset.org/abstracts/search?q=Kareem%20%20Alsharkawy"> Kareem Alsharkawy</a>, <a href="https://publications.waset.org/abstracts/search?q=Robert%20Warner"> Robert Warner</a>, <a href="https://publications.waset.org/abstracts/search?q=Karthikeyan%20Srinivasan"> Karthikeyan Srinivasan</a>, <a href="https://publications.waset.org/abstracts/search?q=Haitham%20Khalil"> Haitham Khalil</a>, <a href="https://publications.waset.org/abstracts/search?q=Ruth%20Waters"> Ruth Waters</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: The cessation of elective surgery, particularly breast reconstruction, continue to be affected by the COVID-19 pandemic. The restructuring of medical services and staff redeployment severely affected the ability to return to normality for surgical specialties. The aim of this study is to determine the decline in breast reconstruction affected by the COVID-19 pandemic in a tertiary center. Methods: A retrospective review of breast reconstruction cases (autologous, non-autologous) or mastectomies Pre- COVID (March 2019-March 2020) and during COVID (March 2020- March 2021) at Queen Elizabeth Hospital, Birmingham, were collated. Data included patient demographics, BMI, previous and recent reconstruction, length of hospital stay, and mastectomies, including risk-reducing. Results: The number of patients who had breast reconstruction was significantly lower during COVID (n=62) compared to pre-COVID (n=199). The mean age (pre-COVID 51, COVID 59 years), BMI (Pre-COVID and COVID = 27), previous reconstruction (pre-COVID n=101, 51%, COVID n=33, 53%) and length hospital stay was less during COVID (3 days) compared to Pre-COVID (4 days). The proportion of risk-reducing mastectomies and reconstruction during COVID (32%, n=20) were higher than pre-COVID (21%, n=41). A higher proportion rate of autologous reconstruction (DIEP 56, TRAM 17) Pre-COVID compared to COVID (DIEP 22, TRAM 7). Implant reconstructions were higher during COVID (n=19, 31%) than pre-COVID (n=31, 16%). Conclusion: The lack of regular provision for breast reconstruction continues to decline during the pandemic. This will have a tremendous impact on waiting lists without a timeline for reconstruction to offer patients. An international survey highlights the disparities in offering breast reconstruction and strategies to rectify this issue. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20reconstruction" title="breast reconstruction">breast reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=COVID-19%20pandemic" title=" COVID-19 pandemic"> COVID-19 pandemic</a>, <a href="https://publications.waset.org/abstracts/search?q=mastectomy" title=" mastectomy"> mastectomy</a>, <a href="https://publications.waset.org/abstracts/search?q=autologous" title=" autologous"> autologous</a>, <a href="https://publications.waset.org/abstracts/search?q=implant" title=" implant"> implant</a> </p> <a href="https://publications.waset.org/abstracts/141759/the-impact-of-covid-19-on-reconstructive-breast-surgery-and-future-prospective" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141759.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">221</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">761</span> Complex Technology of Virtual Reconstruction: The Case of Kazan Imperial University of XIX-Early XX Centuries</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=L.%20K.%20Karimova">L. K. Karimova</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20I.%20Shariukova"> K. I. Shariukova</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20A.%20Kirpichnikova"> A. A. Kirpichnikova</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20A.%20Razuvalova"> E. A. Razuvalova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article deals with technology of virtual reconstruction of Kazan Imperial University of XIX - early XX centuries. The paper describes technologies of 3D-visualization of high-resolution models of objects of university space, creation of multi-agent system and connected with these objects organized database of historical sources, variants of use of technologies of immersion into the virtual environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3D-reconstruction" title="3D-reconstruction">3D-reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-agent%20system" title=" multi-agent system"> multi-agent system</a>, <a href="https://publications.waset.org/abstracts/search?q=database" title=" database"> database</a>, <a href="https://publications.waset.org/abstracts/search?q=university%20space" title=" university space"> university space</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20reconstruction" title=" virtual reconstruction"> virtual reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20heritage" title=" virtual heritage"> virtual heritage</a> </p> <a href="https://publications.waset.org/abstracts/41436/complex-technology-of-virtual-reconstruction-the-case-of-kazan-imperial-university-of-xix-early-xx-centuries" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41436.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">270</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">760</span> 3D Human Body Reconstruction Based on Multiple Viewpoints</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiahe%20Liu">Jiahe Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=HongyangYu"> HongyangYu</a>, <a href="https://publications.waset.org/abstracts/search?q=Feng%20Qian"> Feng Qian</a>, <a href="https://publications.waset.org/abstracts/search?q=Miao%20Luo"> Miao Luo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this study was to improve the effects of human body 3D reconstruction. The MvP algorithm was adopted to obtain key point information from multiple perspectives. This algorithm allowed the capture of human posture and joint positions from multiple angles, providing more comprehensive and accurate data. The study also incorporated the SMPL-X model, which has been widely used for human body modeling, to achieve more accurate 3D reconstruction results. The use of the MvP algorithm made it possible to observe the reconstructed object from multiple angles, thus reducing the problems of blind spots and missing information. This algorithm was able to effectively capture key point information, including the position and rotation angle of limbs, providing key data for subsequent 3D reconstruction. Compared with traditional single-view methods, the method of multi-view fusion significantly improved the accuracy and stability of reconstruction. By combining the MvP algorithm with the SMPL-X model, we successfully achieved better human body 3D reconstruction effects. The SMPL-X model is highly scalable and can generate highly realistic 3D human body models, thus providing more detail and shape information. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3D%20human%20reconstruction" title="3D human reconstruction">3D human reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-view" title=" multi-view"> multi-view</a>, <a href="https://publications.waset.org/abstracts/search?q=joint%20point" title=" joint point"> joint point</a>, <a href="https://publications.waset.org/abstracts/search?q=SMPL-X" title=" SMPL-X"> SMPL-X</a> </p> <a href="https://publications.waset.org/abstracts/173747/3d-human-body-reconstruction-based-on-multiple-viewpoints" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173747.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">70</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">759</span> 3D Object Model Reconstruction Based on Polywogs Wavelet Network Parametrization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Othmani">Mohamed Othmani</a>, <a href="https://publications.waset.org/abstracts/search?q=Yassine%20Khlifi"> Yassine Khlifi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a technique for compact three dimensional (3D) object model reconstruction using wavelet networks. It consists to transform an input surface vertices into signals,and uses wavelet network parameters for signal approximations. To prove this, we use a wavelet network architecture founded on several mother wavelet families. POLYnomials WindOwed with Gaussians (POLYWOG) wavelet families are used to maximize the probability to select the best wavelets which ensure the good generalization of the network. To achieve a better reconstruction, the network is trained several iterations to optimize the wavelet network parameters until the error criterion is small enough. Experimental results will shown that our proposed technique can effectively reconstruct an irregular 3D object models when using the optimized wavelet network parameters. We will prove that an accurateness reconstruction depends on the best choice of the mother wavelets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3d%20object" title="3d object">3d object</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=parametrization" title=" parametrization"> parametrization</a>, <a href="https://publications.waset.org/abstracts/search?q=polywog%20wavelets" title=" polywog wavelets"> polywog wavelets</a>, <a href="https://publications.waset.org/abstracts/search?q=reconstruction" title=" reconstruction"> reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20networks" title=" wavelet networks"> wavelet networks</a> </p> <a href="https://publications.waset.org/abstracts/49814/3d-object-model-reconstruction-based-on-polywogs-wavelet-network-parametrization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49814.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">284</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=sparse%20reconstruction&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=sparse%20reconstruction&page=3">3</a></li> <li class="page-item"><a class="page-link" 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