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Статті в журналах з теми "Visual Computing (VIC)"
Koch, C., H. T. Wang, and B. Mathur. "Computing motion in the primate's visual system." Journal of Experimental Biology 146, no. 1 (September 1, 1989): 115–39. http://dx.doi.org/10.1242/jeb.146.1.115.
Повний текст джерелаXue-Ming, Li, Li Fang-Hua, and Fan Hai-Fu. "A revised version of the program VEC (visual computing in electron crystallography)." Chinese Physics B 18, no. 6 (June 2009): 2459–63. http://dx.doi.org/10.1088/1674-1056/18/6/056.
Повний текст джерелаLi, XiaoYong, QinYang Yu, Yong Zhang, JinWei Dai, and BaoCai Yin. "Visual Analytic Method for Students’ Association via Modularity Optimization." Applied Sciences 10, no. 8 (April 18, 2020): 2813. http://dx.doi.org/10.3390/app10082813.
Повний текст джерелаHokkanen, J. E. "Visual simulations, artificial animals and virtual ecosystems." Journal of Experimental Biology 202, no. 23 (December 1, 1999): 3477–84. http://dx.doi.org/10.1242/jeb.202.23.3477.
Повний текст джерелаNaranjo, Diana M., José R. Prieto, Germán Moltó, and Amanda Calatrava. "A Visual Dashboard to Track Learning Analytics for Educational Cloud Computing." Sensors 19, no. 13 (July 4, 2019): 2952. http://dx.doi.org/10.3390/s19132952.
Повний текст джерелаKENDER, JOHN R. "VISUAL INTERFACES TO COMPUTERS: A SYSTEMS-ORIENTED FIRST COURSE IN RELIABLE CONTROL VIA IMAGERY ("VISUAL INTERFACES")." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 05 (August 2001): 869–84. http://dx.doi.org/10.1142/s0218001401001209.
Повний текст джерелаJiang, Ming-xin, Xian-xian Luo, Tao Hai, Hai-yan Wang, Song Yang, and Ahmed N. Abdalla. "Visual Object Tracking in RGB-D Data via Genetic Feature Learning." Complexity 2019 (May 2, 2019): 1–8. http://dx.doi.org/10.1155/2019/4539410.
Повний текст джерелаSun, Linfeng, Zhongrui Wang, Jinbao Jiang, Yeji Kim, Bomin Joo, Shoujun Zheng, Seungyeon Lee, Woo Jong Yu, Bai-Sun Kong, and Heejun Yang. "In-sensor reservoir computing for language learning via two-dimensional memristors." Science Advances 7, no. 20 (May 2021): eabg1455. http://dx.doi.org/10.1126/sciadv.abg1455.
Повний текст джерелаStokes, John F., and Marlene A. Devine. "Mirp: A Wearable Tool for Evaluating Effectiveness of Information Display." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 6 (September 2005): 728–31. http://dx.doi.org/10.1177/154193120504900602.
Повний текст джерелаGong, Dawei, Zhiheng He, Xiaolong Ye, and Ziyun Fang. "Visual Saliency Detection for Over-Temperature Regions in 3D Space via Dual-Source Images." Sensors 20, no. 12 (June 17, 2020): 3414. http://dx.doi.org/10.3390/s20123414.
Повний текст джерелаДисертації з теми "Visual Computing (VIC)"
Hernandez, Cherstin, and Maya Kronman. "Feedback på din hemmaträning : En analys av designförslag för ett korrigerande verktyg via projektion." Thesis, Högskolan Kristianstad, Fakulteten för ekonomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-22002.
Повний текст джерелаIn this paper, design for visual corrective feedback through projection in at-home-exercising environments is compared to see what design and what design combinations would be usable and most understandable. With the Covid 19 pandemic it has become more actual to start exercising from home instead of going to gyms and increase the infection risk of the virus. However, with most homes not having all the helpful tools that the gyms might have (eg. mirrors and instructors), leads to practitioners not exercising the correct way which in turn might lead to the practitioners receiving injuries rather than the desired results. Early on in the research on digital tools for at home exercising, a knowledge gap came to view. Research and drafts do exist but solutions for at home exercising were rare to nonexistent. With the help of previous research and course literature as a base for design, three proposals were made. Those proposals were used in qualitative interviews to judge and critique the presented proposals. After the interviews, the presented design proposals were reevaluated to give a usable and understandable design proposal of visual corrective feedback through projection.
Borke, Lukas. "Dynamic Clustering and Visualization of Smart Data via D3-3D-LSA." Doctoral thesis, Humboldt-Universität zu Berlin, 2017. http://dx.doi.org/10.18452/18307.
Повний текст джерелаWith the growing popularity of GitHub, the largest host of source code and collaboration platform in the world, it has evolved to a Big Data resource offering a variety of Open Source repositories (OSR). At present, there are more than one million organizations on GitHub, among them Google, Facebook, Twitter, Yahoo, CRAN, RStudio, D3, Plotly and many more. GitHub provides an extensive REST API, which enables scientists to retrieve valuable information about the software and research development life cycles. Our research pursues two main objectives: (I) provide an automatic OSR categorization system for data science teams and software developers promoting discoverability, technology transfer and coexistence; (II) establish visual data exploration and topic driven navigation of GitHub organizations for collaborative reproducible research and web deployment. To transform Big Data into value, in other words into Smart Data, storing and processing of the data semantics and metadata is essential. Further, the choice of an adequate text mining (TM) model is important. The dynamic calibration of metadata configurations, TM models (VSM, GVSM, LSA), clustering methods and clustering quality indices will be shortened as "smart clusterization". Data-Driven Documents (D3) and Three.js (3D) are JavaScript libraries for producing dynamic, interactive data visualizations, featuring hardware acceleration for rendering complex 2D or 3D computer animations of large data sets. Both techniques enable visual data mining (VDM) in web browsers, and will be abbreviated as D3-3D. Latent Semantic Analysis (LSA) measures semantic information through co-occurrence analysis in the text corpus. Its properties and applicability for Big Data analytics will be demonstrated. "Smart clusterization" combined with the dynamic VDM capabilities of D3-3D will be summarized under the term "Dynamic Clustering and Visualization of Smart Data via D3-3D-LSA".
CHIANG, LIN HSING, and 林鋅江. "Design Of The Web Based Visual Framing System Base On JavaScript via the Cloud Computing Concept." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/68451495001883737356.
Повний текст джерела國立臺北教育大學
數位科技設計學系(含玩具與遊戲設計碩士班)
101
With the progress of the times and technological advancement, information transmission and integration of applications is an indispensable part of modern life. The thought of "over the network people can quickly obtain the information they need", is no longer satisfactory. People want to not only get information over the network, but also to process and integrate information to get solutions at faster speed and lower cost. Scholars and the computing industry are making efforts toward such needs. And the concept of "cloud computing" is formed in order to achieve this goal. This thesis is a research on the technology of cloud computing that is "capable of making drawing with browser, and without any extra component". The research is based on the concept of cloud computing and the use of the JavaScript as well as the Ajax as development core. The main focus is on the creation of a visualized online framing system which helps users do their frame works without the need to install any software (ex. Microsoft Word, Adobe Dreamweaver and so on…) and to attach any extra components (ex. Adobe Flash, Microsoft Silverlight, ActiveX and so on…) to their browser. Just only with the browser users can do their works which can only be done with multiple software in the past. And this is an integration of multiple functions in a single web interface.
Частини книг з теми "Visual Computing (VIC)"
Luigini, A., G. A. Massari, S. Vattano, C. Pellegatta, and F. Luce. "Visual Culture and Cultural Heritage: ViC-CH a Synthesis Between Digital Representation and Heritage Experience." In Advances in Intelligent Systems and Computing, 288–302. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12240-9_32.
Повний текст джерелаKotarski, Wiesław, Krzysztof Gdawiec, and Agnieszka Lisowska. "Polynomiography via Ishikawa and Mann Iterations." In Advances in Visual Computing, 305–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33179-4_30.
Повний текст джерелаGalasso, Fabio, and Joan Lasenby. "Shape from Texture Via Fourier Analysis." In Advances in Visual Computing, 803–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89639-5_77.
Повний текст джерелаYang, Xingwei, Nagesh Adluru, Longin Jan Latecki, Xiang Bai, and Zygmunt Pizlo. "Symmetry of Shapes Via Self-similarity." In Advances in Visual Computing, 561–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89646-3_55.
Повний текст джерелаEbrahimpour, Mohammad K., and David C. Noelle. "Fast Object Localization via Sensitivity Analysis." In Advances in Visual Computing, 209–20. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33723-0_17.
Повний текст джерелаFan, Jianping, Hangzai Luo, Aoying Zhou, and Daniel A. Keim. "Personalized News Video Recommendation Via Interactive Exploration." In Advances in Visual Computing, 380–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89646-3_37.
Повний текст джерелаMiller, Corey A., and Thomas J. Walls. "Passive 3D Scene Reconstruction via Hyperspectral Imagery." In Advances in Visual Computing, 413–22. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14249-4_39.
Повний текст джерелаMa, Yunqian, S. B. Damelin, O. Masoud, and N. Papanikolopoulos. "Activity Recognition Via Classification Constrained Diffusion Maps." In Advances in Visual Computing, 1–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11919476_1.
Повний текст джерелаWang, Tinghuai, Guangming Wang, Kuan Eeik Tan, and Donghui Tan. "Hyperspectral Image Classification via Pyramid Graph Reasoning." In Advances in Visual Computing, 707–18. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64556-4_55.
Повний текст джерелаDaptardar, Ajay H., and James A. Storer. "Content-Based Image Retrieval Via Vector Quantization." In Advances in Visual Computing, 502–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11595755_61.
Повний текст джерелаТези доповідей конференцій з теми "Visual Computing (VIC)"
Sun, Zhongqian, Hongxun Yao, Shengping Zhang, and Xin Sun. "Robust visual tracking via context objects computing." In 2011 18th IEEE International Conference on Image Processing (ICIP 2011). IEEE, 2011. http://dx.doi.org/10.1109/icip.2011.6116564.
Повний текст джерелаGebejes, Ana, Miguel Angel Martinez Domingo, Ville Heikkinen, and Ivana Tomic. "Reflectance recovery for coated printed color samples via multiangular RGB camera measurements." In 2013 Colour and Visual Computing Symposium (CVCS). IEEE, 2013. http://dx.doi.org/10.1109/cvcs.2013.6626287.
Повний текст джерелаBottoni, P., P. Quattrocchi, and D. Ventriglia. "Constraining Concrete Syntax via Metamodel Information." In Visual Languages and Human-Centric Computing (VL/HCC'06). IEEE, 2006. http://dx.doi.org/10.1109/vlhcc.2006.17.
Повний текст джерелаSidorov, Oleksii. "Novel Approach to Uniformization of a Color Space via Generic Deep Learning-Based Transformation." In 2018 Colour and Visual Computing Symposium (CVCS). IEEE, 2018. http://dx.doi.org/10.1109/cvcs.2018.8496586.
Повний текст джерелаDuan Xiping, Liu Jiafeng, and Tang Xianglong. "Visual tracking via weighted sparse representation." In 2014 International Conference on Intelligent Computing and Internet of Things (ICIT). IEEE, 2015. http://dx.doi.org/10.1109/icaiot.2015.7111543.
Повний текст джерелаZhou, Kang, Chi Guo, and Huyin Zhang. "Visual Navigation via Reinforcement Learning and Relational Reasoning." In 2021 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/IOP/SCI). IEEE, 2021. http://dx.doi.org/10.1109/swc50871.2021.00027.
Повний текст джерелаКучерова, Кристина, Kristina Kucherova, Сергей Мещеряков, Sergey Mescheryakov, Дмитрий Щемелинин, and Dmitriy Schemelinin. "Analysis and visualization of big data in globally distributed cloud computing monitoring systems." In 29th International Conference on Computer Graphics, Image Processing and Computer Vision, Visualization Systems and the Virtual Environment GraphiCon'2019. Bryansk State Technical University, 2019. http://dx.doi.org/10.30987/graphicon-2019-1-74-77.
Повний текст джерелаDesimone, Robert. "Neuromechanisms of visual perception and attention in monkeys." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/oam.1989.tut1.
Повний текст джерелаDiprose, J. P. "End user robot programming via visual languages." In 2011 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC 2011). IEEE, 2011. http://dx.doi.org/10.1109/vlhcc.2011.6070406.
Повний текст джерелаLi, Wei, and Meng Ding. "Visual tracking via sparse coding and spectral residual." In 2017 International Conference on Progress in Informatics and Computing (PIC). IEEE, 2017. http://dx.doi.org/10.1109/pic.2017.8359536.
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