Auswahl der wissenschaftlichen Literatur zum Thema „Motion data“
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Zeitschriftenartikel zum Thema "Motion data":
Lv, Na, Yan Huang, Zhi Quan Feng und Jing Liang Peng. „A Survey on Motion Capture Data Retrieval“. Applied Mechanics and Materials 556-562 (Mai 2014): 2944–47. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2944.
Chiu, H. C., F. J. Wu, C. J. Lin, H. C. Huang und C. C. Liu. „Effects of rotation motions on strong-motion data“. Journal of Seismology 16, Nr. 4 (01.04.2012): 829–38. http://dx.doi.org/10.1007/s10950-012-9301-z.
Manns, Martin, Michael Otto und Markus Mauer. „Measuring Motion Capture Data Quality for Data Driven Human Motion Synthesis“. Procedia CIRP 41 (2016): 945–50. http://dx.doi.org/10.1016/j.procir.2015.12.068.
Dong, Ran, Dongsheng Cai und Soichiro Ikuno. „Motion Capture Data Analysis in the Instantaneous Frequency-Domain Using Hilbert-Huang Transform“. Sensors 20, Nr. 22 (16.11.2020): 6534. http://dx.doi.org/10.3390/s20226534.
Spratt, D. „SP-0030 Against the motion: Data, data, data“. Radiotherapy and Oncology 158 (Mai 2021): S20. http://dx.doi.org/10.1016/s0167-8140(21)06470-7.
I-Chen Lin, Jen-Yu Peng, Chao-Chih Lin und Ming-Han Tsai. „Adaptive Motion Data Representation with Repeated Motion Analysis“. IEEE Transactions on Visualization and Computer Graphics 17, Nr. 4 (April 2011): 527–38. http://dx.doi.org/10.1109/tvcg.2010.87.
Et.al, JIBUM JUNG. „Use of Human Motion Data to Train Wearable Robots“. Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, Nr. 6 (11.04.2021): 807–11. http://dx.doi.org/10.17762/turcomat.v12i6.2100.
Kashima, T. „Characteristics of Ground Motions and Strong Motion Data of Buildings“. Concrete Journal 50, Nr. 1 (2012): 19–22. http://dx.doi.org/10.3151/coj.50.19.
Miura, Takeshi, Takaaki Kaiga, Naho Matsumoto, Hiroaki Katsura, Takeshi Shibata, Katsubumi Tajima und Hideo Tamamoto. „Characterization of Motion Capture Data by Motion Speed Variation“. IEEJ Transactions on Electronics, Information and Systems 133, Nr. 4 (2013): 906–7. http://dx.doi.org/10.1541/ieejeiss.133.906.
Choi, Myung Geol, und Taesoo Kwon. „Motion rank: applying page rank to motion data search“. Visual Computer 35, Nr. 2 (27.03.2018): 289–300. http://dx.doi.org/10.1007/s00371-018-1498-6.
Dissertationen zum Thema "Motion data":
Kröger, Viktor. „Classification in Functional Data Analysis : Applications on Motion Data“. Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184963.
Främre korsbandsskador är vanliga och välkända skador, speciellt bland idrottsutövare. Skadornakräver ofta operationer och långa rehabiliteringsprogram, och kan leda till funktionell nedsättningoch återskador (Marshall et al., 1977). Målet med det här arbetet är att utforska möjligheten attklassificera knän utifrån funktionalitet, där utfallet är känt. Detta genom att använda olika typerav modeller, och genom att testa olika indelningar av grupper. Datat som används är insamlatunder ett prestandatest, där personer hoppat på ett ben med rörelsesensorer på kroppen. Deninsamlade datan representerar position över tid, och betraktas som funktionell data.Med funktionell dataanalys (FDA) kan en process analyseras som en kontinuerlig funktion av tid,istället för att reduceras till ett ändligt antal datapunkter. FDA innehåller många användbaraverktyg, men även utmaningar. En funktionell observation kan till exempel deriveras, ett händigtverktyg som inte återfinns i den multivariata verktygslådan. Hastigheten och accelerationen kandå beräknas utifrån den insamlade datan. Hur "likhet" är definierat, å andra sidan, är inte likauppenbart som med punkt-data. I det här arbetet används FDA för att klassificera knärörelsedatafrån en långtidsuppföljningsstudie av främre korsbandsskador.I detta arbete studeras både funktionella kärnklassificerare och k-närmsta grannar-metoder, och ut-för signifikanstest av modellträffsäkerheten genom omprovtagning. Vidare kan modellerna urskiljaolika egenskaper i datat, beroende på hur närhet definieras. Ensemblemetoder används i ett försökatt nyttja mer av informationen, men lyckas inte överträffa någon av de enskilda modellerna somutgör ensemblen. Vidare så visas också att klassificering på optimerade deldefinitionsmängder kange en högre förklaringskraft än klassificerare som använder hela definitionsmängden.
Tanco, L. Molina. „Human motion synthesis from captured data“. Thesis, University of Surrey, 2002. http://epubs.surrey.ac.uk/844411/.
Miller, Iain. „Finding associations in motion capture data“. Thesis, University of the West of Scotland, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.729427.
Gonzalez, Rojas Paloma (Paloma Francisca). „Space and motion : data based rules of public space pedestrian motion“. Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/99288.
Title as it appears in MIT Commencement Exercises program, June 5, 2015: Space and motion : the case of pedestrian in public spaces. Cataloged from PDF version of thesis.
Includes bibliographical references (pages 106-107).
The understanding of space relies on motion, as we experience space by crossing it. While in motion we sense the environment in time, interacting with space. The vision of this thesis is to incorporate people's motion into architecture design process, enabled by technology. Simulation tools that introduce human motion into the design process in early stages are rare to nonexistent. Available tools are typically used for deterministically visualizing figures and simulating pedestrians with the goal of analyzing emergency exits or egress. Such simulations are built without consideration for non-goal oriented interaction with space; this presents a gap for design. Additionally, simulations are generally governed by assumptions regarding people's motion behavior or by analogous models such as collision avoidance methods. However, the use of data from people can elucidate spatial behavior. Advancements in depth camera sensors and computer vision algorithms have eased the task of tracking human movements to millimetric precision. This thesis proposes two main ideas: creating statistics from people's motion data for grounding simulations and measuring such motion in relation to space, developing a Space- Motion Metric. This metric takes pedestrian motion and spatial features as input, seeks actions composed by speed, time, gestures, direction, shape and scale. The actions are elaborated as Space-Motion Rules through substantial data analysis. The non-prescriptive combination of the rules generates a non-deterministic behavior focused on design. This research maps, quantifies, and formulates pedestrian motion correlation with space and questions the role of data for projecting what space could be.
by Paloma Gonzalez Rojas.
S.M.
Williams, Ben H. „Extracting motion primitives from natural handwriting data“. Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/3221.
Rogers, Bennett Lee. „Query-by-example for motion capture data“. Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/42255.
Includes bibliographical references (p. 57-58).
Motion capture datasets are employed widely in animation research and industry, however there currently exists no efficient way to index and search this data for diversified use. Motion clips are generally searched by filename or keywords, neither of which incorporates knowledge of actions in the clip aside from those listed in the descriptions. We present a method for indexing and searching a large database of motion capture clips that allows for fast insertion and query-by-example. Over time, more motions can be added to the index, incrementally increasing its value. The result is a tool that reduces the amount of time spent gathering new data for motion applications, and increases the utility of existing motion clips.
by Bennett Lee Rogers.
S.M.
Wang, Zhao. „Motion capture data processing, retrieval and recognition“. Thesis, Bournemouth University, 2018. http://eprints.bournemouth.ac.uk/31038/.
Hosseini, Babak [Verfasser]. „Interpretable analysis of motion data / Babak Hosseini“. Bielefeld : Universitätsbibliothek Bielefeld, 2021. http://d-nb.info/1237815509/34.
Larsson, Albin. „MC.d.o.t : Motion capture data och dess tillgänglighet“. Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-9622.
Svensson, Niclas. „Structure from Motion with Unstructured RGBD Data“. Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302553.
Följande examensarbete behandlar ämnet djupassisterad Struktur genom Rörelse (eng. SfM). Vid klassisk SfM är målet att återskapa en 3D scen, endast med hjälp av en sekvens av oordnade RGB bilder. I djupassiterad SfM adderas djupinformationen till problemformulering och följaktligen har ett system som kan motta RGBD bilder skapats. Problemet har lösts genom att modifiera en befintlig SfM- mjukvara och mer specifikt dess Buntjustering (eng. BA). Resultatet från den modifierade mjukvaran jämförs med resultatet av originalutgåvan för att dra slutsatser rådande modifikationens påverkan på prestandan. Resultaten visar huvudsakligen två saker. Först och främst, den modifierade mjukvaran producerar resultat med högre noggrannhet i de allra flesta fall. Skillnaden är som allra störst när bilderna är tagna från endast en liten sektor som omringar scenen. Data med brus kan dock försämra systemets prestanda aningen jämfört med orginalsystemet. För det andra, så minskar exekutionstiden betydligt. Slutligen diskuteras hur mjukvaran kan vidareutvecklas för att ytterligare förbättra resultaten.
Bücher zum Thema "Motion data":
Qu, Tongbin. Traffic-load forecasting using weigh-in-motion data. [Austin, TX]: Center for Transportation Research, Bureau of Engineering Research, University of Texas at Austin, 1997.
Liu, Dale. Next generation SSH2 implementation: Securing data in motion. Burlington, MA: Syngress Pub., 2008.
Schumacher, Patrik. Digital Hadid: Landscapes in motion. Basel: Boston, 2004.
Müller, Meinard. Information retrieval for music and motion. New York: Springer, 2007.
Cronin, Meghan. Mooring motion correction of SYNOP central array current meter data. Narragansett, R.I: University of Rhode Island, Graduate School of Oceanography, 1992.
IEEE Workshop on Motion and Video Computing (2002 Orlando, Fla.). Workshop on Motion and Video Computing: (MOTION 2002) : 5-6 December, 2002, Orlando, Florida : proceedings. Los Alamitos, Calif: IEEE Computer Society, 2002.
Hein, Günter W. A model comparison in vertical crustal motion estimation using leveling data. Rockville, MD: National Oceanic and Atmospheric Administration, National Ocean Service, Charting and Geodetic Services, 1986.
Hein, Gumlunter W. A model comparison in vertical crustal motion estimation using leveling data. Rockville, MD: National Oceanic and Atmospheric Administration, National Ocean Service, Charting and Geodetic Services, 1986.
Cunagin, Wiley D. Use of weigh-in-motion systems for data collection and enforcement. Washington, D.C: Transportation Research Board, National Research Council, 1986.
Chen, Liming, Boulbaba Ben Amor und Faouzi Ghorbel, Hrsg. Representations, Analysis and Recognition of Shape and Motion from Imaging Data. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19816-9.
Buchteile zum Thema "Motion data":
Lerner, Alon, Yiorgos Chrysanthou, Ariel Shamir und Daniel Cohen-Or. „Data Driven Evaluation of Crowds“. In Motion in Games, 75–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10347-6_7.
Kokaram, Anil C. „Heuristics for Reconstructing Missing Data“. In Motion Picture Restoration, 119–50. London: Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-3485-5_6.
Buchanan, William J. „Motion Video Compression“. In Advanced Data Communications and Networks, 97–109. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4419-8670-2_7.
Benter, Martin, und Peter Kuhlang. „Analysing Body Motions Using Motion Capture Data“. In Advances in Intelligent Systems and Computing, 128–40. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20040-4_12.
Bakir, Ahmed, Gheorghe Chesler und Manny de la Torriente. „Using Core Motion to Save Motion Data“. In Program the Internet of Things with Swift for iOS, 99–117. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1194-6_4.
Kokaram, Anil C. „Model Based Reconstruction for Missing Data“. In Motion Picture Restoration, 151–200. London: Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-3485-5_7.
Eargle, John M. „Typical Motion Picture Screen Losses“. In Electroacoustical Reference Data, 266–67. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2027-6_129.
Lan, Rongyi, Huaijiang Sun und Mingyang Zhu. „Text-Like Motion Representation for Human Motion Retrieval“. In Intelligent Science and Intelligent Data Engineering, 72–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36669-7_10.
Buchanan, Bill. „Motion Video Compression“. In Handbook of Data Communications and Networks, 83–95. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4757-0905-6_8.
Müller, Meinard, und Tido Röder. „A Relational Approach to Content-based Analysis of Motion Capture Data“. In Human Motion, 477–506. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6693-1_20.
Konferenzberichte zum Thema "Motion data":
Andrews, Sheldon, Marc Jarvis und Paul G. Kry. „Data-driven Fingertip Appearance for Interactive Hand Simulation“. In Motion. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2522628.2522903.
Heithausen, Cordula, Maria Meyer, Max Blaser und Jens-Rainer Ohm. „Temporal Prediction of Motion Parameters with Interchangeable Motion Models“. In 2017 Data Compression Conference (DCC). IEEE, 2017. http://dx.doi.org/10.1109/dcc.2017.30.
Roberts, Derek, und Ying Zhu. „Motion Tracking for Volumetric Motion Capture Data“. In 2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems Workshops (MASSW). IEEE, 2019. http://dx.doi.org/10.1109/massw.2019.00025.
Faraway, Julian J. „Data-Based Motion Prediction“. In Digital Human Modeling for Design and Engineering Conference and Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2003. http://dx.doi.org/10.4271/2003-01-2229.
Ismaeil, I. R., A. Docef, F. Kossentini und R. Ward. „Motion estimation using long-term motion vector prediction“. In Proceedings DCC'99 Data Compression Conference (Cat. No. PR00096). IEEE, 1999. http://dx.doi.org/10.1109/dcc.1999.785688.
Ahmad, R., M. B. Che Omar, M. S. Yaacob, M. Hussein, M. Z. Md Zain und M. Y. Abdullah. „Modeling of human motion through motion captured data“. In 2008 International Symposium on Information Technology. IEEE, 2008. http://dx.doi.org/10.1109/itsim.2008.4632064.
Matsui, Ayaka, Kazumasa Miura und Seiichiro Katsura. „Robust motion-copying system using motion-data memory“. In 2013 IEEE 22nd International Symposium on Industrial Electronics (ISIE). IEEE, 2013. http://dx.doi.org/10.1109/isie.2013.6563816.
Ren, Cheng, Xiaoyong Lei und Guofeng Zhang. „Motion Data Retrieval from Very Large Motion Databases“. In 2011 International Conference on Virtual Reality and Visualization (ICVRV). IEEE, 2011. http://dx.doi.org/10.1109/icvrv.2011.50.
Hu, Yueqi, Shuangyuan Wu, Shihong Xia, Jinghua Fu und Wei Chen. „Motion track: Visualizing variations of human motion data“. In 2010 IEEE Pacific Visualization Symposium (PacificVis). IEEE, 2010. http://dx.doi.org/10.1109/pacificvis.2010.5429596.
Ahmmed, Ashek, und Mark Pickering. „Motion Hint Field with Content Adaptive Motion Model for High Efficiency Video Coding (HEVC)“. In 2016 Data Compression Conference (DCC). IEEE, 2016. http://dx.doi.org/10.1109/dcc.2016.93.
Berichte der Organisationen zum Thema "Motion data":
Deng D. P. LONGITUDINAL MOTION - DATA PRESENTATION. Office of Scientific and Technical Information (OSTI), Mai 1991. http://dx.doi.org/10.2172/1151258.
Lades, M. Motion description for data compression and classification. Office of Scientific and Technical Information (OSTI), Februar 1998. http://dx.doi.org/10.2172/8300.
Wei, Ting, und Jon Fricker. Weigh-In-Motion Data Checking and Imputation. West Lafayette, IN: Purdue University, 2003. http://dx.doi.org/10.5703/1288284313349.
NA. Ground Motion Saturation Evaluation (GMSE) Data Needs Workshop. Office of Scientific and Technical Information (OSTI), Juli 2004. http://dx.doi.org/10.2172/837690.
Dai, Chengxin. Exploring Data Quality of Weigh-In-Motion Systems. Portland State University Library, Januar 2000. http://dx.doi.org/10.15760/etd.1018.
Nichols, Andrew, und Darcy Bullock. Quality Control Procedures for Weigh-in-Motion Data. West Lafayette, IN: Purdue University, 2004. http://dx.doi.org/10.5703/1288284313299.
Sanders, M. L. Description of ground motion data processing codes: Volume 3. Office of Scientific and Technical Information (OSTI), Februar 1988. http://dx.doi.org/10.2172/60459.
Cassidy, J. F., A. Rosenberger und G. C. Rogers. Strong motion seismograph networks, data, and research in Canada. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2008. http://dx.doi.org/10.4095/225729.
Lin, L., und J. Adams. Compilation of digital strong motion data for eastern Canada. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2010. http://dx.doi.org/10.4095/285374.
Briggs, Michael J., Stephen T. Maynord, Charles R. Nickles und Terry N. Waller. Charleston Harbor Ship Motion Data Collection and Squat Analysis. Fort Belvoir, VA: Defense Technical Information Center, März 2004. http://dx.doi.org/10.21236/ada457976.