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Robust Principal Component Analysis for Background Estimation of Particle Image Velocimetry Data [article]

Ahmadreza Baghaie
2019 arXiv   pre-print
This is achieved by utilizing Robust Principal Component Analysis (RPCA) applied to the data matrix, generated by aggregating the vectorized PIV frames.  ...  Formulating the assumptions as an optimization problem, Augmented Lagrange Multiplier (ALM) method is used for decomposing the data matrix into the low-rank and sparse components.  ...  (a) sample original frame, (b) min-removal method, (c) POD-based method, (d)proposed method.  ... 
arXiv:1908.06047v1 fatcat:5rvubfq75be5hkcldqr76euqum

A Hybrid Based Approach for Object Tracking in Video

Amith. R, V.N. Manjunath Aradhya
2016 IRA-International Journal of Technology & Engineering (ISSN 2455-4480)  
Many traditional tracking algorithms fails to track the moving objects in real-time, this paper proposes a robust method to overcome the issue, based on the combination of particle filter and Principal  ...  Similarity degree between the object model and the prediction obtained from particle filter is used to update the feature vector to handle occlusion and complex background in video frames.  ...  Proposed Method Particle Filter: Particle filter is the generic algorithm for a function optimization where the solutions search space is searched using particles.  ... 
doi:10.21013/jte.v4.n1.p2 fatcat:wrkuyhbrszhdvas7wznetb3ble

Gradient-based Approach for Determination of Oscillating Flow Fields in PIV [chapter]

Atsushi Nomura, Koichi Okada, Hidetoshi Miike, Hidemi Yam
2010 Engineering the Future  
The gradient-based approach is generally inappropriate to high flow speed, in comparison to the matching-based method.  ...  The dependence obtained for ACM differs from that for the gradient-based methods. The RMS error measures for ACM monotonically decrease as the density of particles increases.  ...  Gradient-based Approach for Determination of Oscillating Flow Fields in PIV, Engineering the Future, Laszlo Dudas (Ed.), ISBN: 978-953-307-210-4, InTech, Available from:  ... 
doi:10.5772/10370 fatcat:pe6tkg5sxvg6fl32aanmfwrgwu

Crowd Motion Analysis Based on Social Force Graph with Streak Flow Attribute

Shaonian Huang, Dongjun Huang, Mansoor Ahmed Khuhro
2015 Journal of Electrical and Computer Engineering  
The graph is segmented into particle groups to represent the similar motion patterns of crowd video.  ...  Extensive experiments on challenging datasets show that the proposed method achieves preferable results in the application of crowd motion segmentation and abnormal behavior detection.  ...  based on the size of the components.  ... 
doi:10.1155/2015/492051 fatcat:3jnaqka6pbg7tpw74k6dqz55uu

A Spatio-Spectral Algorithm for Robust and Scalable Object Tracking in Videos [chapter]

Alireza Tavakkoli, Mircea Nicolescu, George Bebis
2010 Lecture Notes in Computer Science  
The main component of the proposed framework takes detected objects as inputs and generates their appearance models, maintains them and tracks these individuals within the video.  ...  In this work we propose a mechanism which looks at processing the low-level visual information present in video frames and prepares mid-level tracking trajectories of objects of interest within the video  ...  The first component is responsible for generating and maintaining a model for the objects while the second process searches for potential new locations for these objects in the new frames.  ... 
doi:10.1007/978-3-642-17277-9_17 fatcat:wrxdltfb7fc3xmz6ftkkxwecvy

Identification of loose particles using deep learning convolutional network

Cai Meng, Yanggang Li, Guoqiang Zhang, Changxing Zhao
2020 Vibroengineering PROCEDIA  
Loose particles inside components can be threats to their reliabilities. Automatic identification of loose particles' material has great significance for finding the source of them.  ...  Machine learning methods based on hand-craft features have been widely applied on this problem.  ...  Particle impact noise detection (PIND) test is a major method for detecting the presence of loose particles left inside components.  ... 
doi:10.21595/vp.2020.21614 fatcat:v5j7jnovvbbv7j36e52o6uy7jy

Interactive Particle Visualization with Advanced Shading Models using Lazy Evaluation [article]

Christiaan P. Gribble, Steven G. Parker
2007 Eurographics Symposium on Parallel Graphics and Visualization  
Particle-based simulation methods are used to model a wide range of complex phenomena and to solve timedependent problems of various scales.  ...  We explore the performance characteristics of this approach and demonstrate its effectiveness using several large particle datasets.  ...  (Frame rates reflect performance while servicing outstanding texture generation requests.) which argues for an ordering based on timestamps.  ... 
doi:10.2312/egpgv/egpgv07/037-044 fatcat:gbwlfxmqnbfnrl4l6i3h6iwhfe

Optimization of Sports Fitness Management System Based on Internet of Health Things

Yongquan Tang, Dehua Wang
2020 IEEE Access  
A IoHTs modeling method for component collaboration is proposed, and the IoHTs component library is designed according to the proposed modeling method.  ...  new method for communication research in the Internet of Health Things field.  ...  There are three ways to generate vital sign data for each component: the first is to generate vital sign data based on a function model; the second is to obtain vital sign data collected by the hardware  ... 
doi:10.1109/access.2020.3039508 fatcat:xeggbouryvhw5n4aq4dnipkdtq

Real-Time Rendering of Point Based Water Surfaces [chapter]

Kei Iwasaki, Yoshinori Dobashi, Fujiichi Yoshimoto, Tomoyuki Nishita
2006 Lecture Notes in Computer Science  
To address this, we present a real-time method for rendering water surfaces resulting from particle-based simulation.  ...  Therefore, it is difficult to render the result of the particle-based simulation at interactive frame rates.  ...  Acknowledgements We thank Prometech Software Inc. for providing MPS simulation data and thank Kaori Ono and Yoshitaka Moro for their help.  ... 
doi:10.1007/11784203_9 fatcat:jk5gxqynhzdw3dprm34lnzcobe

Active Contour-Based Visual Tracking by Integrating Colors, Shapes, and Motions Using Level Sets

Suvarna D.
2015 International Journal on Recent and Innovation Trends in Computing and Communication  
For the contour-based tracking initialization, we use an optical flow-based algorithm for the automatically initializing contours at the first frame.  ...  The main components of our framework consist of the contour-based tracking initialization, colour-based contour evolution, the adaptive shape-based contour evolution for the non-periodic motions, the dynamic  ...  object for the next frame.  ... 
doi:10.17762/ijritcc2321-8169.150136 fatcat:vaee2pdlo5eqbkvppgsunjojqu

General object tracking with a component-based target descriptor

Simone Frintrop
2010 2010 IEEE International Conference on Robotics and Automation  
In this paper, we present a component-based visual object tracker for mobile platforms.  ...  The core of the technique is a component-based descriptor that captures the structure and appearance of a target in a flexible way.  ...  All methods use the same particle filter approach for tracking and a color-based observation model.  ... 
doi:10.1109/robot.2010.5509638 dblp:conf/icra/Frintrop10 fatcat:crbpbhmbffehplifxi6ei3gike

Efficient Hand Articulations Tracking using Adaptive Hand Model and Depth map [article]

Byeongkeun Kang, Yeejin Lee, Truong Q. Nguyen
2015 arXiv   pre-print
Our contributions are: (a) propose adaptive hand model to consider different hand shapes of users without generating personalized hand model; (b) improve the highly efficient frame initialization for robust  ...  We also initialize hand pose at each frame using finger detection and classification.  ...  Conclusion We present an efficient hand articulations tracking system for mobile and wearable devices which do not have high performance GPUs.  ... 
arXiv:1510.00981v3 fatcat:u66rdmv335enphb7g3sne3gt7a

Particle Filter Algorithm for Object Tracking in Video Sequence Based on Chromatic Information

Prachi R. Narkhede
2018 International Journal for Research in Applied Science and Engineering Technology  
In this paper, an idea for tracking an object in a video sequence using particle filer is proposed.  ...  This paper deals with object detection by different methods including K-means clustering and seeded region growing and merging approach to derive the object features and object tracking by color based  ...  Resampling The particle filtering process described above is repeated for each frame in the video. After a few iteration of the process, many low weight particles are generated.  ... 
doi:10.22214/ijraset.2018.4667 fatcat:j2ovg5um2zhplc73kscda2mrti

X-ray Imaging Analysis of Silo Flow Parameters Based on Trace Particles Using Targeted Crowdsourcing

Andrzej Romanowski, Piotr Łuczak, Krzysztof Grudzień
2019 Sensors  
This paper presents a novel method for tomographic measurement and data analysis based on crowdsourcing. X-ray radiography imaging was initially applied to determine silo flow parameters.  ...  Therefore, we used crowdsourcing for human-driven annotation of the trace particles.  ...  John Speller for his valuable help with English proofreading. Conflicts of Interest: The authors declare no conflict of interest. Sensors 2019, 19, 3317  ... 
doi:10.3390/s19153317 fatcat:tbzkfxlezjdfhobk6x7cehizr4

Super-resolution PIV using multi-frame displacement information

Hua Yang, Zhenxing Ouyang, Yunkang Cao, Zhen Yang, Zhouping Yin
2021 14th International Symposium on Particle Image Velocimetry  
Multi-frame particle images collected by the developed system are used as the input low-resolution images for the following novel SR reconstruction algorithm.  ...  Subsequently, we present a Bayesian-based multi-frame SR (BMFSR) reconstruction algorithm to obtain an SR particle image.  ...  Multi-frame imaging system As mentioned above, traditional and deep learning SR reconstruction methods generate HR flow fields based on LR single-frame data.  ... 
doi:10.18409/ispiv.v1i1.161 fatcat:kpzykqietjfhtlhip3c7uycypy
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