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Bibliografias temáticas / Motion data

Literatura científica selecionada sobre o tema "Motion data"

Autor: Grafiati

Publicado: 23 de junho de 2021

Última modificação: 11 de fevereiro de 2022

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Artigos de revistas
Teses / dissertações
Livros
Capítulos de livros
Trabalhos de conferências
Relatórios de organizações

Artigos de revistas sobre o assunto "Motion data":

1

Lv, Na, Yan Huang, Zhi Quan Feng e Jing Liang Peng. "A Survey on Motion Capture Data Retrieval". Applied Mechanics and Materials 556-562 (maio de 2014): 2944–47. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2944.

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With the rapid development of motion capture technology, large motion capture databases are established. How to effectively retrieve the motions from huge amounts of motion data has become a hot topic in computer animation. In this paper, we give a survey on current motion capture data retrieval methods and point out some still existing problems at the end.

2

Chiu, H. C., F. J. Wu, C. J. Lin, H. C. Huang e C. C. Liu. "Effects of rotation motions on strong-motion data". Journal of Seismology 16, n.º 4 (1 de abril de 2012): 829–38. http://dx.doi.org/10.1007/s10950-012-9301-z.

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3

Manns, Martin, Michael Otto e 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.

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4

Dong, Ran, Dongsheng Cai e Soichiro Ikuno. "Motion Capture Data Analysis in the Instantaneous Frequency-Domain Using Hilbert-Huang Transform". Sensors 20, n.º 22 (16 de novembro de 2020): 6534. http://dx.doi.org/10.3390/s20226534.

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Motion capture data are widely used in different research fields such as medical, entertainment, and industry. However, most motion researches using motion capture data are carried out in the time-domain. To understand human motion complexities, it is necessary to analyze motion data in the frequency-domain. In this paper, to analyze human motions, we present a framework to transform motions into the instantaneous frequency-domain using the Hilbert-Huang transform (HHT). The empirical mode decomposition (EMD) that is a part of HHT decomposes nonstationary and nonlinear signals captured from the real-world experiments into pseudo monochromatic signals, so-called intrinsic mode function (IMF). Our research reveals that the multivariate EMD can decompose complicated human motions into a finite number of nonlinear modes (IMFs) corresponding to distinct motion primitives. Analyzing these decomposed motions in Hilbert spectrum, motion characteristics can be extracted and visualized in instantaneous frequency-domain. For example, we apply our framework to (1) a jump motion, (2) a foot-injured gait, and (3) a golf swing motion.

5

Spratt, D. "SP-0030 Against the motion: Data, data, data". Radiotherapy and Oncology 158 (maio de 2021): S20. http://dx.doi.org/10.1016/s0167-8140(21)06470-7.

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6

I-Chen Lin, Jen-Yu Peng, Chao-Chih Lin e Ming-Han Tsai. "Adaptive Motion Data Representation with Repeated Motion Analysis". IEEE Transactions on Visualization and Computer Graphics 17, n.º 4 (abril de 2011): 527–38. http://dx.doi.org/10.1109/tvcg.2010.87.

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7

Et.al, JIBUM JUNG. "Use of Human Motion Data to Train Wearable Robots". Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, n.º 6 (11 de abril de 2021): 807–11. http://dx.doi.org/10.17762/turcomat.v12i6.2100.

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Development of wearable robots is accelerating. Walking robots mimic human behavior and must operate without accidents. Human motion data are needed to train these robots. We developed a system for extracting human motion data and displaying them graphically.We extracted motion data using a Perception Neuron motion capture system and used the Unity engine for the simulation. Several experiments were performed to demonstrate the accuracy of the extracted motion data.Of the various methods used to collect human motion data, markerless motion capture is highly inaccurate, while optical motion capture is very expensive, requiring several high-resolution cameras and a large number of markers. Motion capture using a magnetic field sensor is subject to environmental interference. Therefore, we used an inertial motion capture system. Each movement sequence involved four and was repeated 10 times. The data were stored and standardized. The motions of three individuals were compared to those of a reference person; the similarity exceeded 90% in all cases. Our rehabilitation robot accurately simulated human movements: individually tailored wearable robots could be designed based on our data. Safe and stable robot operation can be verified in advance via simulation. Walking stability can be increased using walking robots trained via machine learning algorithms.

8

Kashima, T. "Characteristics of Ground Motions and Strong Motion Data of Buildings". Concrete Journal 50, n.º 1 (2012): 19–22. http://dx.doi.org/10.3151/coj.50.19.

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9

Miura, Takeshi, Takaaki Kaiga, Naho Matsumoto, Hiroaki Katsura, Takeshi Shibata, Katsubumi Tajima e Hideo Tamamoto. "Characterization of Motion Capture Data by Motion Speed Variation". IEEJ Transactions on Electronics, Information and Systems 133, n.º 4 (2013): 906–7. http://dx.doi.org/10.1541/ieejeiss.133.906.

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10

Choi, Myung Geol, e Taesoo Kwon. "Motion rank: applying page rank to motion data search". Visual Computer 35, n.º 2 (27 de março de 2018): 289–300. http://dx.doi.org/10.1007/s00371-018-1498-6.

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Mais fontes

Teses / dissertações sobre o assunto "Motion data":

1

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.

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Anterior cruciate knee ligament injuries are common and well known, especially amongst athletes.These injuries often require surgeries and long rehabilitation programs, and can lead to functionloss and re-injuries (Marshall et al., 1977). This work aims to explore the possibility of applyingsupervised classification on knee functionality, using different types of models, and testing differentdivisions of classes. The data used is gathered through a performance test, where individualsperform one-leg hops with motion sensors attached to their bodies. The obtained data representsthe position over time, and is considered functional data.With functional data analysis (FDA), a process can be analysed as a continuous function of time,instead of being reduced to finite data points. FDA includes many useful tools, but also somechallenges. A functional observation can for example be differentiated, a handy tool not found inthe multivariate tool-box. The speed, and acceleration, can then be calculated from the obtaineddata. How to define "similarity" is, on the other hand, not as obvious as with points. In this work,an FDA-approach is taken on classifying knee kinematic data, from a long-term follow-up studyon knee ligament injuries.This work studies kernel functional classifiers, and k-nearest neighbours models, and performssignificance tests on the model accuracy, using re-sampling methods. Additionally, depending onhow similarity is defined, the models can distinguish different features of the data. Attempts atutilising more information through incorporation of ensemble-methods, does not exceed the singlemodels it is created from. Further, it is shown that classification on optimised sub-domains, canbe superior to classifiers using the full domain, in terms of predictive power.
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.

2

Tanco, L. Molina. "Human motion synthesis from captured data". Thesis, University of Surrey, 2002. http://epubs.surrey.ac.uk/844411/.

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Animation of human motion is one of the most challenging topics in computer graphics. This is due to the large number of degrees of freedom of the body and to our ability to detect unnatural motion. Keyframing and interpolation remains the form of animation that is preferred by most animators because of the control and flexibility it provides. However this is a labour intensive process that requires skills that take years to acquire. Human motion capture techniques provide accurate measurement of the motion of a performer that can be mapped onto an animated character to provide strikingly natural animation. This raises the problem of how to allow an animator to modify captured movement to produce a desired animation whilst preserving the natural quality. This thesis introduces a new approach to the animation of human motion based on combining the flexibility of keyframing with the visual quality of motion capture data. In particular it addresses the problem of synthesising natural inbetween motion for sparse keyframes. This thesis proposes to obtain this motion by sampling high quality human motion capture data. The problem of keyframe interpolation is formulated as a search problem in a graph. This presents two difficulties: The complexity of the search makes it impractical for the large databases of motion capture required to model human motion. The second difficulty is that the global temporal structure in the data may not be preserved in the search. To address these difficulties this thesis introduces a layered framework that both reduces the complexity of the search and preserves the global temporal structure of the data. The first layer is a simplification of the graph obtained by clustering methods. This layer enables efficient planning of the search for a path between start and end keyframes. The second layer directly samples segments of the original motion data to synthesise realistic inbetween motion for the keyframes. A number of additional contributions are made including novel representations for human motion, pose similarity cost functions, dynamic programming algorithms for efficient search and quantitative evaluation methods. Results of realistic inbetween motion are presented with databases of up to 120 sequences (35000 frames). Key words: Human Motion Synthesis, Motion Capture, Character Animation, Graph Search, Clustering, Unsupervised Learning, Markov Models, Dynamic Programming.

3

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.

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4

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.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Architecture, 2015.
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.

5

Williams, Ben H. "Extracting motion primitives from natural handwriting data". Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/3221.

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Humans and animals can plan and execute movements much more adaptably and reliably than current computers can calculate robotic limb trajectories. Over recent decades, it has been suggested that our brains use motor primitives as blocks to build up movements. In broad terms a primitive is a segment of pre-optimised movement allowing a simplified movement planning solution. This thesis explores a generative model of handwriting based upon the concept of motor primitives. Unlike most primitive extraction studies, the primitives here are time extended blocks that are superimposed with character specific offsets to create a pen trajectory. This thesis shows how handwriting can be represented using a simple fixed function superposition model, where the variation in the handwriting arises from timing variation in the onset of the functions. Furthermore, it is shown how handwriting style variations could be due to primitive function differences between individuals, and how the timing code could provide a style invariant representation of the handwriting. The spike timing representation of the pen movements provides an extremely compact code, which could resemble internal spiking neural representations in the brain. The model proposes an novel way to infer primitives in data, and the proposed formalised probabilistic model allows informative priors to be introduced providing a more accurate inference of primitive shape and timing.

6

Rogers, Bennett Lee. "Query-by-example for motion capture data". Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/42255.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
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.

7

Wang, Zhao. "Motion capture data processing, retrieval and recognition". Thesis, Bournemouth University, 2018. http://eprints.bournemouth.ac.uk/31038/.

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Character animation plays an essential role in the area of featured film and computer games. Manually creating character animation by animators is both tedious and inefficient, where motion capture techniques (MoCap) have been developed and become the most popular method for creating realistic character animation products. Commercial MoCap systems are expensive and the capturing process itself usually requires an indoor studio environment. Procedural animation creation is often lacking extensive user control during the generation progress. Therefore, efficiently and effectively reusing MoCap data can brings significant benefits, which has motivated wider research in terms of machine learning based MoCap data processing. A typical work flow of MoCap data reusing can be divided into 3 stages: data capture, data management and data reusing. There are still many challenges at each stage. For instance, the data capture and management often suffer from data quality problems. The efficient and effective retrieval method is also demanding due to the large amount of data being used. In addition, classification and understanding of actions are the fundamental basis of data reusing. This thesis proposes to use machine learning on MoCap data for reusing purposes, where a frame work of motion capture data processing is designed. The modular design of this framework enables motion data refinement, retrieval and recognition. The first part of this thesis introduces various methods used in existing motion capture processing approaches in literature and a brief introduction of relevant machine learning methods used in this framework. In general, the frameworks related to refinement, retrieval, recognition are discussed. A motion refinement algorithm based on dictionary learning will then be presented, where kinematical structural and temporal information are exploited. The designed optimization method and data preprocessing technique can ensure a smooth property for the recovered result. After that, a motion refinement algorithm based on matrix completion is presented, where the low-rank property and spatio-temporal information is exploited. Such model does not require preparing data for training. The designed optimization method outperforms existing approaches in regard to both effectiveness and efficiency. A motion retrieval method based on multi-view feature selection is also proposed, where the intrinsic relations between visual words in each motion feature subspace are discovered as a means of improving the retrieval performance. A provisional trace-ratio objective function and an iterative optimization method are also included. A non-negative matrix factorization based motion data clustering method is proposed for recognition purposes, which aims to deal with large scale unsupervised/semi-supervised problems. In addition, deep learning models are used for motion data recognition, e.g. 2D gait recognition and 3D MoCap recognition. To sum up, the research on motion data refinement, retrieval and recognition are presented in this thesis with an aim to tackle the major challenges in motion reusing. The proposed motion refinement methods aim to provide high quality clean motion data for downstream applications. The designed multi-view feature selection algorithm aims to improve the motion retrieval performance. The proposed motion recognition methods are equally essential for motion understanding. A collection of publications by the author of this thesis are noted in publications section.

8

Hosseini, Babak [Verfasser]. "Interpretable analysis of motion data / Babak Hosseini". Bielefeld : Universitätsbibliothek Bielefeld, 2021. http://d-nb.info/1237815509/34.

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9

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.

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Hårdvara kan bli gammal, program kan sluta utvecklas, filer som skapats från sådan hårdvara respektive mjukvara kan bli oanvändbara med tiden. Samt att hålla ordning på många individuella filer kan i längden bli jobbigt för användare. Med en databasorienterad lagrinsgslösning kan olika API:er användas för att göra data kompatibel med flera olika verktyg och program, samt att det kan användas för att skapa en centraliserad lösning för att enkelt hålla ordning på information. Bland databaser finns det två primära grupperingar: SQL och NoSQL. Detta arbete ämnar undersöka vilken typ som passar för att hantera motion capture data. Tester har utförts på SQLs MySQL och NoSQLs Neo4j. Neo4j som är specialiserad för att hantera data som motion capture data. Resultatet från testningarna är förvånande nog att MySQL hanterar motion capture data bättre än Neo4j. Ytterligare arbeten för att undersöka fler varianter av databaser för en mer komplett bild föreslås.

10

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.

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This thesis covers the topic of depth- assisted Structure from Motion (SfM). When performing classic SfM, the goal is to reconstruct a 3D scene using only a set of unstructured RGB images. What is attempted to be achieved in this thesis is adding the depth dimension to the problem formulation, and consequently create a system that can receive a set of RGBD images. The problem has been addressed by modifying an already existing SfM pipeline and in particular, its Bundle Adjustment (BA) process. Comparisons between the modified framework and the baseline framework resulted in conclusions regarding the impact of the modifications. The results show mainly two things. First of all, the accuracy of the framework is increased in most situations. The difference is the most significant when the captured scene only is covered from a small sector. However, noisy data can cause the modified pipeline to decrease in performance. Secondly, the run time of the framework is significantly reduced. A discussion of how to modify other parts of the pipeline is covered in the conclusion of the report.
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.

Mais fontes

Livros sobre o assunto "Motion data":

1

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.

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2

Liu, Dale. Next generation SSH2 implementation: Securing data in motion. Burlington, MA: Syngress Pub., 2008.

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3

Schumacher, Patrik. Digital Hadid: Landscapes in motion. Basel: Boston, 2004.

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4

Müller, Meinard. Information retrieval for music and motion. New York: Springer, 2007.

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5

Cronin, Meghan. Mooring motion correction of SYNOP central array current meter data. Narragansett, R.I: University of Rhode Island, Graduate School of Oceanography, 1992.

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6

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.

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7

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.

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8

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.

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9

Cunagin, Wiley D. Use of weigh-in-motion systems for data collection and enforcement. Washington, D.C: Transportation Research Board, National Research Council, 1986.

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10

Chen, Liming, Boulbaba Ben Amor e Faouzi Ghorbel, eds. 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.

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Mais fontes

Capítulos de livros sobre o assunto "Motion data":

1

Lerner, Alon, Yiorgos Chrysanthou, Ariel Shamir e 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.

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2

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.

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3

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.

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4

Benter, Martin, e 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.

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5

Bakir, Ahmed, Gheorghe Chesler e 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.

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6

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.

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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.

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8

Lan, Rongyi, Huaijiang Sun e 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.

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9

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.

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10

Müller, Meinard, e 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.

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Trabalhos de conferências sobre o assunto "Motion data":

1

Andrews, Sheldon, Marc Jarvis e 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.

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2

Heithausen, Cordula, Maria Meyer, Max Blaser e 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.

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3

Roberts, Derek, e 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.

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4

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.

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5

Ismaeil, I. R., A. Docef, F. Kossentini e 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.

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6

Ahmad, R., M. B. Che Omar, M. S. Yaacob, M. Hussein, M. Z. Md Zain e 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.

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7

Matsui, Ayaka, Kazumasa Miura e 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.

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8

Ren, Cheng, Xiaoyong Lei e 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.

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9

Hu, Yueqi, Shuangyuan Wu, Shihong Xia, Jinghua Fu e 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.

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10

Ahmmed, Ashek, e 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.

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Relatórios de organizações sobre o assunto "Motion data":

1

Deng D. P. LONGITUDINAL MOTION - DATA PRESENTATION. Office of Scientific and Technical Information (OSTI), maio de 1991. http://dx.doi.org/10.2172/1151258.

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2

Lades, M. Motion description for data compression and classification. Office of Scientific and Technical Information (OSTI), fevereiro de 1998. http://dx.doi.org/10.2172/8300.

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3

Wei, Ting, e Jon Fricker. Weigh-In-Motion Data Checking and Imputation. West Lafayette, IN: Purdue University, 2003. http://dx.doi.org/10.5703/1288284313349.

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4

NA. Ground Motion Saturation Evaluation (GMSE) Data Needs Workshop. Office of Scientific and Technical Information (OSTI), julho de 2004. http://dx.doi.org/10.2172/837690.

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5

Dai, Chengxin. Exploring Data Quality of Weigh-In-Motion Systems. Portland State University Library, janeiro de 2000. http://dx.doi.org/10.15760/etd.1018.

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6

Nichols, Andrew, e Darcy Bullock. Quality Control Procedures for Weigh-in-Motion Data. West Lafayette, IN: Purdue University, 2004. http://dx.doi.org/10.5703/1288284313299.

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7

Sanders, M. L. Description of ground motion data processing codes: Volume 3. Office of Scientific and Technical Information (OSTI), fevereiro de 1988. http://dx.doi.org/10.2172/60459.

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8

Cassidy, J. F., A. Rosenberger e 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.

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9

Lin, L., e 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.

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10

Briggs, Michael J., Stephen T. Maynord, Charles R. Nickles e Terry N. Waller. Charleston Harbor Ship Motion Data Collection and Squat Analysis. Fort Belvoir, VA: Defense Technical Information Center, março de 2004. http://dx.doi.org/10.21236/ada457976.

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