Virtual Simulation of Aerobics Movement Based on Vision

2014 ◽  
Vol 989-994 ◽  
pp. 1997-2000 ◽  
Author(s):  
Xiao Hui Wang
Keyword(s):  
3D Reconstruction ◽  
Large Scale ◽  
Reconstruction Algorithm ◽  
Feature Points ◽  
Reconstruction Algorithms ◽  
Dynamic Features ◽  
Motion State ◽  
The Stability ◽  
Motion Images ◽  
Complex Movement

In aerobics, the motion state randomness of key feature points is great, so it is difficult to establish an accurate dynamic model for sports' shape base. Traditional 3D reconstruction algorithms use fixes shape base which hardly expresses the change parameters of complex movement and motion law of large-scale dynamic features, thereby leading to non-realistic reconstruction results. The paper proposes a new reconstruction algorithm for aerobics 3D motion images that corrects the neighborhood system of feature points by motion parameters until the parameter is stable to ensure accuracy and the stability of correction. The simulation results show that, the proposed algorithm avoids drawbacks of sports reconstruction results caused by the great randomness of aerobics' motion state, thereby complete 3D reconstruction for aerobics' motion images.

A Reconstruction Model for 3D Human Motion Based on Images Processing

2014 ◽  
Vol 556-562 ◽  
pp. 5021-5023
Author(s):  
Zhi Yuan Yang
Keyword(s):  
Large Scale ◽  
Reconstruction Algorithm ◽  
Human Motion ◽  
Reconstruction Algorithms ◽  
Dynamic Features ◽  
3D Motion ◽  
Images Processing ◽  
Motion Images ◽  
Reconstruction Model ◽  
Complex Movement

Traditional 3D reconstruction algorithms use fixes shape base which hardly expresses the change parameters of complex movement and motion law of large-scale dynamic features, thereby leading to non-realistic reconstruction results. The paper proposes a new reconstruction algorithm for 3D motion images that corrects the neighborhood system of feature points by motion parameters and reasons number base K to ensure accuracy. The simulation results show that, the proposed algorithm avoids drawbacks of sports reconstruction results caused by the great randomness of motion state, thereby complete 3D motion images' reconstruction.

scholarly journals A Poisson Equation-Based Method for 3D Reconstruction of Animated Images

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ziang Lei
Keyword(s):  
3D Reconstruction ◽  
Real Time ◽  
Large Scale ◽  
Least Square ◽  
Reconstruction Method ◽  
Feature Points ◽  
Computing Power ◽  
To Come ◽  
Point Detection ◽  
Considerable Period

3D reconstruction techniques for animated images and animation techniques for faces are important research in computer graphics-related fields. Traditional 3D reconstruction techniques for animated images mainly rely on expensive 3D scanning equipment and a lot of time-consuming postprocessing manually and require the scanned animated subject to remain in a fixed pose for a considerable period. In recent years, the development of large-scale computing power of computer-related hardware, especially distributed computing, has made it possible to come up with a real-time and efficient solution. In this paper, we propose a 3D reconstruction method for multivisual animated images based on Poisson’s equation theory. The calibration theory is used to calibrate the multivisual animated images, obtain the internal and external parameters of the camera calibration module, extract the feature points from the animated images of each viewpoint by using the corner point detection operator, then match and correct the extracted feature points by using the least square median method, and complete the 3D reconstruction of the multivisual animated images. The experimental results show that the proposed method can obtain the 3D reconstruction results of multivisual animation images quickly and accurately and has certain real-time and reliability.

scholarly journals Sparse reconstruction for fluorescence molecular tomography via a fast iterative algorithm

2014 ◽  
Vol 07 (03) ◽  
pp. 1450008 ◽  
Author(s):  
Jingjing Yu ◽  
Jingxing Cheng ◽  
Yuqing Hou ◽  
Xiaowei He
Keyword(s):  
Iterative Algorithm ◽  
Large Scale ◽  
Learning Strategy ◽  
Imaging Modality ◽  
A Priori ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Fluorescence Molecular Tomography ◽  
Ill Posed ◽  
Priori Information

Fluorescence molecular tomography (FMT) is a fast-developing optical imaging modality that has great potential in early diagnosis of disease and drugs development. However, reconstruction algorithms have to address a highly ill-posed problem to fulfill 3D reconstruction in FMT. In this contribution, we propose an efficient iterative algorithm to solve the large-scale reconstruction problem, in which the sparsity of fluorescent targets is taken as useful a priori information in designing the reconstruction algorithm. In the implementation, a fast sparse approximation scheme combined with a stage-wise learning strategy enable the algorithm to deal with the ill-posed inverse problem at reduced computational costs. We validate the proposed fast iterative method with numerical simulation on a digital mouse model. Experimental results demonstrate that our method is robust for different finite element meshes and different Poisson noise levels.

scholarly journals A parallel and automatically tuned algorithm for multispectral image deconvolution

2019 ◽  
Vol 490 (1) ◽  
pp. 37-49
Author(s):  
R Ammanouil ◽  
A Ferrari ◽  
D Mary ◽  
C Ferrari ◽  
F Loi
Keyword(s):  
Big Data ◽  
Image Reconstruction ◽  
Large Scale ◽  
Parallel Implementation ◽  
Wide Band ◽  
Reconstruction Algorithm ◽  
Intermediate Frequency ◽  
Image Deconvolution ◽  
Reconstruction Algorithms ◽  
Big Data Applications

ABSTRACT In the era of big data, radio astronomical image reconstruction algorithms are challenged to estimate clean images given limited computing resources and time. This article is driven by the need for large-scale image reconstruction for the future Square Kilometre Array (SKA), which will become in the next decades the largest low and intermediate frequency radio telescope in the world. This work proposes a scalable wide-band deconvolution algorithm called MUFFIN, which stands for ‘MUlti Frequency image reconstruction For radio INterferometry’. MUFFIN estimates the sky images in various frequency bands, given the corresponding dirty images and point spread functions. The reconstruction is achieved by minimizing a data fidelity term and joint spatial and spectral sparse analysis regularization terms. It is consequently non-parametric w.r.t. the spectral behaviour of radio sources. MUFFIN algorithm is endowed with a parallel implementation and an automatic tuning of the regularization parameters, making it scalable and well suited for big data applications such as SKA. Comparisons between MUFFIN and the state-of-the-art wide-band reconstruction algorithm are provided.

A new reconstruction algorithm based on temporal neural network and its application in power quality disturbance data

2021 ◽  
pp. 002029402110197
Author(s):  
Yan Liu ◽  
Wei Tang ◽  
Yiduo Luan
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Large Scale ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Reconstruction Accuracy ◽  
Power Quality Disturbance ◽  
Fully Connected ◽  
Reconstruction Model ◽  
Reconstruction Time

The traditional reconstruction algorithms based on p-norm, limited by their reconstruction model and data processing mode, are prone to reconstruction failure or long reconstruction time. In order to break through the limitations, this paper proposes a reconstruction algorithm based on the temporal neural network (TCN). A new reconstruction model based on TCN is first established, which does not need sparse representation and has large-scale parallel processing. Next, a TCN with a fully connected layer and symmetrical zero-padding operation is designed to meet the reconstruction requirements, including non-causality and length-inconsistency. Moreover, the proposed algorithm is constructed and applied to power quality disturbance (PQD) data. Experimental results show that the proposed algorithm can implement the reconstruction task, demonstrating better reconstruction accuracy and less reconstruction time than OMP, ROMP, CoSaMP, and SP. Therefore, the proposed algorithm is more attractive when dictionary design is complicated, or real-time reconstruction is required.

DEVELOPMENT OF THE BASIC CRITERIA FOR RUSSIAN COASTS TYPIFICATION

2017 ◽  
Author(s):  
Рубен Косян ◽  
Ruben Kosyan ◽  
Viacheslav Krylenko ◽  
Viacheslav Krylenko
Keyword(s):  
Coastal Zone ◽  
Coastal Area ◽  
Economic Activity ◽  
Basic Information ◽  
Dynamic Features ◽  
Static State ◽  
Evolutionary Features ◽  
Coastal Features ◽  
The Stability

There are many types of coasts classifications that indicate main coastal features. As a rule, the "static" state of the coasts is considered regardless of their evolutionary features and ways to further transformation. Since the most part of the coastal zone studies aimed at ensuring of economic activity, it is clear that the classification of coast types should indicate total information required by the users. Accordingly, the coast classification should include the criterion, characterizing as dynamic features of the coast and the conditions and opportunities of economic activity. The coast classification, of course, should be based on geomorphological coast typification. Similar typification has been developed by leading scientists from Russia and can be used with minimal modifications. The authors propose to add to basic information (geomorphological type of coast) the evaluative part for each coast sector. It will include the estimation of the coast changes probability and the complexity of the coast stabilization for economic activity. This method will allow to assess the dynamics of specific coastal sections and the processes intensity and, as a result – the stability of the coastal area.

scholarly journals Development and Characterization of Two Types of Surface Displayed Levansucrases for Levan Biosynthesis

Catalysts ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 757
Author(s):  
Huiyi Shang ◽  
Danni Yang ◽  
Dairong Qiao ◽  
Hui Xu ◽  
Yi Cao
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Molecular Weight ◽  
Large Scale ◽  
Surface Display ◽  
Yeast Surface Display ◽  
Fusion Partner ◽  
Whole Cell ◽  
Food Industries ◽  
Yeast Surface ◽  
The Stability

Levan has wide applications in chemical, cosmetic, pharmaceutical and food industries. The free levansucrase is usually used in the biosynthesis of levan, but the poor reusability and low stability of free levansucrase have limited its large-scale use. To address this problem, the surface-displayed levansucrase in Saccharomyces cerevisiae were generated and evaluated in this study. The levansucrase from Zymomonas mobilis was displayed on the cell surface of Saccharomyces cerevisiae EBY100 using a various yeast surface display platform. The N-terminal fusion partner is based on a-agglutinin, and the C-terminal one is Flo1p. The yield of levan produced by these two whole-cell biocatalysts reaches 26 g/L and 34 g/L in 24 h, respectively. Meanwhile, the stability of the surface-displayed levansucrases is significantly enhanced. After six reuses, these two biocatalysts retained over 50% and 60% of their initial activities, respectively. Furthermore, the molecular weight and polydispersity test of the products suggested that the whole-cell biocatalyst of levansucrase displayed by Flo1p has more potentials in the production of levan with low molecular weight which is critical in certain applications. In conclusion, our method not only enable the possibility to reuse the enzyme, but also improves the stability of the enzyme.

Convergence and stability analysis of the half thresholding based few-view CT reconstruction

2020 ◽  
Vol 28 (6) ◽  
pp. 829-847
Author(s):  
Hua Huang ◽  
Chengwu Lu ◽  
Lingli Zhang ◽  
Weiwei Wang
Keyword(s):  
Noise Suppression ◽  
Reconstructed Image ◽  
Reference Image ◽  
Projection Data ◽  
Ct Reconstruction ◽  
Reconstruction Algorithms ◽  
Ill Posed ◽  
Thresholding Algorithm ◽  
The Stability ◽  
Well Posed

AbstractThe projection data obtained using the computed tomography (CT) technique are often incomplete and inconsistent owing to the radiation exposure and practical environment of the CT process, which may lead to a few-view reconstruction problem. Reconstructing an object from few projection views is often an ill-posed inverse problem. To solve such problems, regularization is an effective technique, in which the ill-posed problem is approximated considering a family of neighboring well-posed problems. In this study, we considered the {\ell_{1/2}} regularization to solve such ill-posed problems. Subsequently, the half thresholding algorithm was employed to solve the {\ell_{1/2}} regularization-based problem. The convergence analysis of the proposed method was performed, and the error bound between the reference image and reconstructed image was clarified. Finally, the stability of the proposed method was analyzed. The result of numerical experiments demonstrated that the proposed method can outperform the classical reconstruction algorithms in terms of noise suppression and preserving the details of the reconstructed image.

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