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48 results for “Motion Modelling”
3D Models of 6dof Motion
<p>This repository contains a 3d model (head_6dof.blend) visualizing motions with 6 degrees of freedom. For the head we use a 3D model created with <a href="http://www.makehumancommunity.org">make human</a> under the <a href="https://creativecommons.org/licenses/by-sa/3.0/">CC BY-SA 3.0</a> license. All body parts but the head were removed from the model used. The main model contains the head, all planes, axes, and labels. Examples of rendered images and an animation are included.The model was created with Blender.</p> <p><strong>Nomenclature</strong><br> Names of the axes and planes according to <a href="https://link.springer.com/content/pdf/10.1007/s00405-015-3835-y.pdf">Bremova et al., 2016</a>:</p> <table align="center"> <thead> <tr> <th scope="col">Abreviation</th> <th scope="col">Axis</th> </tr> </thead> <tbody> <tr> <td>IA</td> <td>inter-aural</td> </tr> <tr> <td>HV</td> <td>head-vertical</td> </tr> <tr> <td>NO</td> <td>naso-occipital</td> </tr> </tbody> </table> <p>Colors<br> Colors are taken from the <a href="https://www.nature.com/articles/nmeth.1618.pdf">Bang Wong color palette</a> which is designed to be accessible to people who are colorblind.</p> <table align="center"> <thead> <tr> <th scope="col">Axis</th> <th scope="col">Color</th> <th scope="col">RGB</th> </tr> </thead> <tbody> <tr> <td>IA</td> <td>bluish green</td> <td>000,158,115</td> </tr> <tr> <td>HV</td> <td>blue</td> <td>000,114,178</td> </tr> <tr> <td>NO</td> <td>vermillion</td> <td>213,094,000</td> </tr> <tr> <td>roll</td> <td>reddish purple</td> <td>204,121,167</td> </tr> <tr> <td>pitch</td> <td>orange</td> <td>230,159,000</td> </tr> <tr> <td>yaw</td> <td>sky blue</td> <td>086,180,233</td> </tr> </tbody> </table> <p> </p>
Figure 9 in A new icriodontid conodont cluster with specific mesowear supports an alternative apparatus motion model for Icriodontidae
Figure 9. Model of masticatory motion of icriodontid I elements. A, oblique lateral view; B, 'anterior' view.
Figure 8 in A new icriodontid conodont cluster with specific mesowear supports an alternative apparatus motion model for Icriodontidae
Figure 8. Motion of P1 elements of ozarkodinid apparatuses summarized from the literature. A, Idiognathodus (Pennsylvanian); B, Novispathodus (Early Triassic); C, Wurmiella excavata (Silurian); D, Pseudofurnishius murcianus (Middle–Late Triassic); E, Polygnathus xylus xylus (Middle Devonian). Grey dots mark the pivot point; black arrows indicate the direction of occlusion and interlocking of P1 elements, grey arrows its reversal.
Figure 7. Hypothetical apparatus reconstruction deduced from the element arrangement within the Caudicriodus woschmidti conodont cluster. A, Model 1 in A new icriodontid conodont cluster with specific mesowear supports an alternative apparatus motion model for Icriodontidae
Figure 7. Hypothetical apparatus reconstruction deduced from the element arrangement within the Caudicriodus woschmidti conodont cluster. A, Model 1 with tips of coniform elements pointing dorsally and 'posterior' part of icriodontan elements oriented ventrally. B, Model 2 with tips of coniform elements and 'posterior' part of icriodontan elements oriented ventrally. C, Model 3 with tips of coniform elements pointing ventrally and 'posterior' part of icriodontan elements oriented dorsally. D, Model 4 with tips of coniform elements and 'posterior' part of icriodontan elements oriented ventrally. Coniform elements are arranged in multiple rows.
Figure 5 in A new icriodontid conodont cluster with specific mesowear supports an alternative apparatus motion model for Icriodontidae
Figure 5. Denticle tip wear of icriodontid I elements. A–C, Icriodus aff. michiganus, dextral I element, lateral and oral view; Middle Devonian, Eifel, Germany; sample BL-12-29c-9. D–F, Icriodus sp., dextral I element, lateral and oral view; Middle Devonian, Eifel, Germany; sample BL-12-29c-3. Extent and orientation of tip wear are indicated by dotted lines and arrowheads.
Figure 6 in A new icriodontid conodont cluster with specific mesowear supports an alternative apparatus motion model for Icriodontidae
Figure 6. Diagrams illustrating orientation and direction of denticle tip wear. A, Icriodus aff. michiganus; left-side illustration shows the orientation of the inclined facet plane, right-side illustration the direction of vertically inclined facet; Middle Devonian, Eifel, Germany; sample BL-12-29c-9. B, Icriodus sp. left-side illustration shows the orientation of the inclined facet plane, middle the direction of the vertically inclined facet, and right the orientation and direction of the facet plane of median row denticles; Middle Devonian, Eifel, Germany; sample BL-12-29c-3).
Figure 4. A in A new icriodontid conodont cluster with specific mesowear supports an alternative apparatus motion model for Icriodontidae
Figure 4. A, denticle tip wear of the dextral I element of Caudicriodus woschmidti; Early Devonian, southern Burgenland, Austria; Ki/ 4/2a-1, NHMW 2011/0374/0001. B, detailed view of oral surface of the dextral I element with extent and orientation of tip wear indicated by dotted line and arrow head.
Figure 3 in A new icriodontid conodont cluster with specific mesowear supports an alternative apparatus motion model for Icriodontidae
Figure 3. Chronological listing of notation history for icriodontid apparatus elements. Morphologically similar coniform element types and the icriodontan element evaluated for this study are highlighted in different colours or shades.
Figure 2 in A new icriodontid conodont cluster with specific mesowear supports an alternative apparatus motion model for Icriodontidae
Figure 2. Conodont cluster of Caudicriodus woschmidti, Early Devonian, southern Burgenland, Austria; Ki/4/2a-1, NHMW 2011/0374/ 0001. A, SEM scan of the conodont cluster. B, detailed view of the coniform elements (C1–C5) close to the dextral I element. C, D, computer microtomography-based three-dimensional reconstruction with identification of all elements. E, hypothetical arrangement of all elements preserved within the fused conodont cluster.
Figure 1 in A new icriodontid conodont cluster with specific mesowear supports an alternative apparatus motion model for Icriodontidae
Figure 1. Locality map and section log from the 'Kottwitz' quarry (southern Burgenland, Austria), where the Caudicriodus woschmidti conodont cluster was found.
Dataset for generating LOD3 building models from structure-from-motion and semantic segmentation
<p>This repository contains the codes for computing geometrical digital twins as LOD3 models for buildings, using a structure from motion and semantic segmentation. The methodology hereby implements was presented in the paper [Generating LOD3 building models from structure-from-motion and semantic segmentation" by Pantoja-Rosero et., al. (2022)] (<a href="https://doi.org/10.1016/j.autcon.2022.104430">https://doi.org/10.1016/j.autcon.2022.104430</a>)</p>
X-ray image reconstruction for continuous acquisitions with a generalized motion model: Data
<p>This dataset contains two experimentally measured X-ray scans, one reference scan and one scan in which the object translates while rotating. The reference scan consists of 3600 projection images, with one flat field and dark field image. The roto-translational scan consists of 360 projections, also with a flat field and dark field image. The acquisition files containing all relevant specifications of the scanner and the acquisition settings are also supplied.</p> <p>The code for reconstruction is available at <a href="https://github.com/BenHuyge/RACE">GitHub.</a></p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 8. 3D model of some facial expressions
<p>Face region is separated precisely from video frames by using a segmentation method based on skin color. The depth data corresponding to this separated area is taken for a 3D representation from depth data corresponding to each frame. At the end, a file is prepared for each frame consisting of face points with 6 features: X, Y, depth, red, green and blue color. These data are used for producing a 3D model and a graphical avatar for each frame (Figure 7). Figure 8 shows 3D model of some facial expressions.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 7. Avatar 3D model generation
<p>Face region is separated precisely from video frames by using a segmentation method based on skin color. The depth data corresponding to this separated area is taken for a 3D representation from depth data corresponding to each frame. At the end, a file is prepared for each frame consisting of face points with 6 features: X, Y, depth, red, green and blue color. These data are used for producing a 3D model and a graphical avatar for each frame (Figure 7). Figure 8 shows 3D model of some facial expressions.</p>
WASHTREET. Application of Structure from Motion (SfM) photogrammetric technique to determine surface elevations in an urban drainage physical model.
<p><strong>WASHTREET</strong><strong> - </strong><strong>Application of Structure from Motion (SfM) photogrammetric technique to determine surface elevations in an urban drainage physical model.</strong></p> <p>This dataset contains raw data and surface elevations results from the application of the Structure from Motion (SfM) photogrammetric technique in a 36 m<sup>2</sup> full-scale urban drainage physical model, which is placed in the Hydraulic Laboratory of the Centre for Technological Innovation in Construction and Civil Engineering (CITEEC) at the University of A Coruña (Spain). This work is part of the <a href="https://zenodo.org/communities/washtreet">WASHTREET project</a>, where a series of high-resolution experiments were performed measuring urban surface wash-off and sediment transport through gully pots and pipes under laboratory-controlled conditions. The accurately measurement of the surface elevations is needed for a proper representation of surface flow, which is key in the detachment and transport of solids in the model surface. The dataset was used in the work developed in Naves et al. (2019) (DOI: <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a>)</p> <p>A detailed description of experimental procedure and data collected can be consulted in ‘<em>1_ExperimentalProcedure.pdf’</em>. Raw images taken as input for the SfM software are included in ‘<em>2_RawImages.zip’</em>. Then, the point cloud resulted is provided in ‘<em>3_SFM_RawPointCloud.ply</em>’. This point cloud was processed and the final elevation map with a resolution of 5 mm is included in ‘<em>4_SfM_ElevationMap(m).xyz</em>’.</p> <p>Further details of the physical model and hydraulic and sediment transport experiments can be consulted in the dataset <a href="http://doi.org/10.5281/zenodo.3233918"><em>WASHTREET - Hydraulic, wash-off and sediment transport experimental data</em></a>. In addition, raw data and runoff velocities results obtained using seeded and unseeded Particle Image Velocimetry (PIV) techniques are provided in the dataset <a href="http://www.doi.org/10.5281/zenodo.3239401">WASHTREET - PIV data</a>.</p> <p>The WASHTREET project is being developed in the scope of the PhD thesis of the first author, which is in receipt of a Spanish Ministry of Science, Innovation and Universities predoctoral grant [FPU14/01778]. The project also receive funding from the Spanish Ministry of Science, Innovation and Universities under POREDRAIN project RTI2018-094217-B-C33 (MINECO/FEDER-EU)</p> <p>Derived publications:</p> <ul> <li>Naves, J., Anta, J., Puertas, J., Regueiro-Picallo, M., & Suárez, J. (2019). Using a 2D shallow water model to assess Large-Scale Particle Image Velocimetry (LSPIV) and Structure from Motion (SfM) techniques in a street-scale urban drainage physical model. <em>Journal of Hydrology</em>, <em>575</em>, 54-65. <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a></li> <li>Naves, J., Anta, J., Suárez, J., & Puertas, J. (2020). Hydraulic, wash-off and sediment transport experiments in a full-scale urban drainage physical model. <em>Scientific Data</em>, <em>7</em>(1), 1-13.<a href="http://doi.org/10.1038/s41597-020-0384-z"> https://doi.org/10.1038/s41597-020-0384-z</a></li> </ul>
UAS-SfM data from Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA
<p>Data for:</p> <p>Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA<br>Sean Reilly 1, Matthew L. Clark 2, Lika Loechler 2, Jack Spillane 2, Melina Kozanitas 3, Paris Krause 4, David Ackerly 3, Lisa Patrick Bentley 4, and Imma Oliveras Menor 1,5</p> <p>1 Environmental Change Institute, University of Oxford, Oxford OX1 3QY, UK<br>2 Center for Interdisciplinary Geospatial Analysis, Department of Geography, Environment, and Planning, Sonoma State University, Rohnert Park, CA 94928, USA<br>3 Departments of Integrative Biology and Environmental Science, Policy, and Management, University of California, Berkeley, CA 94720, USA<br>4 Department of Biology, Sonoma State University, Rohnert Park, CA 94928, USA<br>5 AMAP (Botanique et Modélisation de l’Architecture des Plantes et des Végétations), CIRAD, CNRS, INRA, IRD, Université de Montpellier, Montpellier, France</p> <p>Study abstract:</p> <p>There is a pressing need for well-informed management to reduce wildfire hazard and restore fire’s beneficial ecological role in the Mediterranean- and temperate-climate forests of California, USA. These efforts rely upon the accessibility of high spatial and temporal resolution data on biomass and canopy fuel parameters such as canopy base height (CBH), mean canopy height, canopy bulk density (CBD), canopy cover, and leaf area index (LAI). Remote sensing using unoccupied aerial system Structure-from-Motion (UAS-SfM) presents a promising technology for this application due to its accessibility, relatively low cost, and possibility for high temporal cadence. However, to date, this method has not been studied in the complex mosaic of forest types found across California. In this study we examined the capacity of structural and multispectral information obtained from UAS-SfM, in conjunction with machine learning methods, to model aboveground biomass and forest canopy fuel structural parameters using an area-based approach across multiple sites representing a diversity of forest types in California.</p> <p>Based on correlations with field measurements, fuel parameters separated into vertical (biomass, CBH, and mean height) and horizontal (LAI, CBD, canopy cover) groups. UAS-SfM random forest models performed well for modelling the vertical structure canopy fuels parameters (R2 0.69 – 0.75). These models exhibited strong performance in comparison to ALS, as well as when transferred to a novel site. Vertical structure predictors were prominent in these models, and did not improve with the addition of spectral predictors. UAS-SfM random forest models of horizontal structure parameters mainly used raster-based spectral indices (primarily NDVI) and had relatively low performance (R2 0.49 – 0.59). In addition, these models underperformed ALS and had poor performance when applied to a novel site. When applied to a region with widespread UAS-SfM coverage, models from both groups successfully produced contiguous maps that could be used for modelling fire behavior or in management decision making and monitoring.</p> <p>These findings indicate that UAS-SfM, without the need for multispectral sensors, is well suited for mapping area-based vertical-structure canopy parameters across diverse landscapes supporting a wide range of forest types. In contrast, the identification of spectral mean variables for modelling horizontal structure canopy fuels suggests the potential of multi- or hyperspectral sensors or high-resolution satellite imagery for meeting management information needs. </p> <p>Published in Remote Sensing of Environment</p> <p><br>Contents:</p> <p>This repository contains multispectral UAS-SfM data from four sites around California, USA:<br>jcksn: Jackson Demonstration State Forest<br>ltr: LaTour Demonstration State Forest<br>ppwd: Pepperwood Preserve<br>sdlmtn: Saddle Mountain Open Space Preserve</p> <p>Data were collected during a series of campaigns:<br>c1: Pepperwood, 2019-09-01 to 2019-10-15<br>c3: Jackson, 2020-06-15 to 2020-07-02<br>c4: LaTour, 2020-07-07 to 2020-07-17<br>c6: Saddle Mountain, 2020-08-04 to 2020-08-09<br>c9: Jackson, 2021-07-08 to 2021-07-12</p> <p>Data are included in three formats:<br>raw: Raw outputs from Pix4D (spectral and las)<br>reg_grnd, reg_cnpy: Las files with merged multispectral data and classified ground, registered to ALS using either ground points (grnd) or, in cases with insufficient ground points for registration, to the canopy (cnpy)<br>hnrm: Height normalized las files, normalization performed using ALS terrain model</p> <p>File naming structure:<br>site_campaign_flightzone_uas_processedstate</p> <p>See accompanying paper for methods on data collection and processing</p> <p>Data are grouped into zipped folder by product type</p> <p>Funding:</p> <p>Funding for this research was supported by CAL FIRE Forest Health and Forest Legacy (8GG18806) and California State University, Agricultural Research Institute (20-01-106) awards to L.P.B and M.L.C. S.R. was funded by the Rhodes Trust and through the University of Oxford Environmental Change Institute Small Grant Scheme. Pepperwood ground data collection was supported by funding from the Gordon and Betty Moore Foundation and National Science Foundation grants 1754475 and 1835086.</p> <p>Citation:</p> <div> <div>Reilly, S., Clark, M.L., Loechler, L., Spillane, J., Kozanitas, M., Krause, P., Ackerly, D., Bentley, L.P., Menor, I.O., 2024. Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA. Remote Sensing of Environment 312, 114310. <a href="https://doi.org/10.1016/j.rse.2024.114310">https://doi.org/10.1016/j.rse.2024.114310</a></div> </div> <p> </p> <p> </p>
Populations of local direction-selective cells encode global motion patterns generated by self-motion. Data, Code and Model.
<p>Directional tuning of the population of local motion detectors T4/T5 in the visual system of the fruit fly <em>Drosophila melanogaster</em>. Direction tuning and receptive field location was measured by recording responses to visual stimuli containing dark or bright edges/stripes moving into 8 directions. All provided MATLAB scripts were used to analyze and illustrate data show in the manuscript 'Populations of local direction-selective cells encode global motion patterns generated by self-motion.'</p> <p>All data were obtained using <em>in vivo </em>two photon microscopy. Image time series were preprocessed using SIMA python software for motion alignment and further processed using custom written matlab or python code.</p> <p>Please find all relevant information to use the code in the README file.</p>
Data for: Free-Breathing Myocardial T1 Mapping using Inversion-Recovery Radial FLASH and Motion-Resolved Model-Based Reconstruction (Part 1/2)
<p>Magnetic Resonance Imaging measurement data used in our paper about "Free-Breathing Myocardial T1 Mapping using Inversion-Recovery Radial FLASH and Motion-Resolved Model-Based Reconstruction". The data is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>)</p>
Numerical study of the one-dimensional Holstein model using the momentum-space hierarchical equations of motion method
<p>Data on the finite-temperature current-current correlation function of the one-dimensional Holstein model. Data are obtained using the newly developed momentum-space hierarchical equations of motion (HEOM) method. Details on the method development, as well as on the model parameters, will be given as a supplementary material to a journal publication that will be deposited on arXiv. Folders Regime* contain temporal evolution of the current-current correlation function (j_j_real_time.txt), diffusion constant (diffusion_constant.txt), diffusion exponent (diffusion_exponent.txt), and the electron's spread (delta_x.txt). They also contain frequency profiles of the Fourier transformed current-current correlation function (j_j_real_frequency.txt) and dynamical mobility (dynamical_mobility.txt).</p>
Predicting the distribution of serotonergic axons: A supercomputing simulation of reflected fractional Brownian motion in a 3D-mouse brain model
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