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71 results for “simulated images”
AURORA: Learning Action and Reasoning-Centric Image Editing from Videos and Simulations
<p>Dataset for the publication "Learning Action and Reasoning-Centric Image Editing from Videos and Simulations".</p> <p>Details to fully use the dataset: https://github.com/McGill-NLP/AURORA</p> <p> </p> <p>Abstract:</p> <p>An image editing model should be able to perform diverse edits, ranging from object replacement, changing attributes or style, to performing actions or movement, which require many forms of reasoning. Current <em>general</em> instruction-guided editing models have significant shortcomings with action and reasoning-centric edits. Object, attribute or stylistic changes can be learned from visually static datasets. On the other hand, high-quality data for action and reasoning-centric edits is scarce and has to come from entirely different sources that cover e.g. physical dynamics, temporality and spatial reasoning. To this end, we meticulously curate the <strong>A</strong>U<strong>RO</strong>R<strong>A</strong> Dataset (<strong>A</strong>ction-<strong>R</strong>easoning-<strong>O</strong>bject-<strong>A</strong>ttribute), a collection of high-quality training data, human-annotated and curated from videos and simulation engines. We focus on a key aspect of quality training data: triplets (source image, prompt, target image) contain a single meaningful visual change described by the prompt, i.e., <em>truly minimal</em> changes between source and target images. To demonstrate the value of our dataset, we evaluate an <strong>A</strong>U<strong>RO</strong>R<strong>A</strong>-finetuned model on a new expert-curated benchmark (<strong>A</strong>U<strong>RO</strong>R<strong>A-Bench</strong>) covering 8 diverse editing tasks. Our model significantly outperforms previous editing models as judged by human raters. For automatic evaluations, we find important flaws in previous metrics and caution their use for semantically hard editing tasks. Instead, we propose a new automatic metric that focuses on discriminative understanding. We hope that our efforts : (1) curating a quality training dataset and an evaluation benchmark, (2) developing critical evaluations, and (3) releasing a state-of-the-art model, will fuel further progress on general image editing.</p>
Finite element meshes of graphite foam samples for Image-Based Simulation (IBSim) of experimental laser flash analysis
<p>Image-Based Simulation (IBSim) meshes:<br> Finite element mesh of laser flash analysis (LFA) disc samples made of a graphite foam material (KFoam). The IBSim meshes are generated directly from a 3D volumetric image of a graphite foam block. The 3D image was generated with an X-ray tomography scan performed by Dr Llion Evans with Manchester X-ray Imaging Facility equipment, which was funded in part by the EPSRC (grants EP/F007906/1, EP/F001452/1 and EP/I02249X/1). Segmentation of the data into a binarized image was achieved with ImageJ. Conversion of the segmented data to FE mesh was achieved using ScanIP, part of the Simpleware suite of programmes, version 7 (Synopsys Inc., Mountain View, CA, USA).</p> <p>The graphite foam has anisotropic properties partly due to its microstructure. This dataset contains three meshes, one for each alignment along cartesian axes.</p> <p>The FE meshes use the EnSight Gold file format and may be visualised using Paraview (<a href="https://www.paraview.org">https://www.paraview.org</a>).</p> <p>The CT data used for the mesh is available as a separate dataset:</p> <p>This data was used originally for the following publications (please cite if re-using the data):<br> Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Image based in silico characterisation of the effective thermal properties of a graphite foam”, Carbon, Vol. 143, pp. 542-558, 2018. <a href="https://doi.org/10.1016/j.carbon.2018.10.031">https://doi.org/10.1016/j.carbon.2018.10.031</a></p>
Data for Quantitative Validation of Hypervelocity Impact Simulations Using Experimental Image Data
<p>Raw data used to generate the results in the publication, "Quantitative Validation of Hypervelocity Impact Simulations Using Experimental Image Data". Data consists of node positions from SPH simulations and experimental data images.</p>
Data associated with "Imaging and Simulation of Surface Plasmon Polaritons on Layered 2D MXenes"
<p>Data and code for "Imaging and Simulation of Surface Plasmon Polaritons on Layered 2D MXenes"<br>Janek Rieger, Atreyie Ghosh, Joseph L. Spellberg, Calvin Raab, Aishani Mohan, Prakriti P. Joshi, Sarah B. King</p>
AusCAT Simulation Imaging Data
<p>This data is part of the NCLC Radiomics dataset provided by The Cancer Imaging Archive (TCIA) and is</p> <p>released under the Creative Commons Attribution 3.0 Unported License.</p> <p> </p> <p>CAUTION: This data is provided for experimental purposes only. Data is partly synthetically generated.</p> <p> </p> <p>Citations & Data Usage Policy</p> <p>Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution</p> <p>3.0 Unported License under which it has been published. Attribution should include references to</p> <p>the following citations:</p> <p> </p> <p>Dataset Citation</p> <p>Aerts, H. J. W. L., Wee, L., Rios Velazquez, E., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., Hoebers, F., Rietbergen, M. M., Leemans, C. R., Dekker, A., Quackenbush, J., Gillies, R. J., Lambin, P. (2019). Data From NSCLC-Radiomics [Data set]. The Cancer Imaging Archive. <https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI></p> <p> </p> <p>Publication Citation</p> <p>Aerts, H. J. W. L., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., Hoebers, F., Rietbergen, M. M., Leemans, C. R., Dekker, A., Quackenbush, J., Gillies, R. J., Lambin, P. (2014, June 3). Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications. Nature Publishing Group. <https://doi.org/10.1038/ncomms5006></p> <p> </p> <p>TCIA Citation</p> <p>Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. <https://doi.org/10.1007/s10278-013-9622-7></p> <p> </p>
SRDTrans dataset: simulated calcium imaging data sampled at 30 Hz under different SNRs
<p>SRDTrans dataset: simulated calcium imaging data sampled at 30 Hz under different SNRs.</p>
SRDTrans dataset: simulated calcium imaging data at different imaging speeds
<p>SRDTrans dataset: simulated calcium imaging data at different imaging speeds</p>
Ab initio multiple spawning simulations for "Rehybridization dynamics into the pericyclic minimum of an electrocyclic reaction imaged in real-time"
<p>60 ICs a(0.82)-2SA-cas(6,4)-SCF/6-31G* using AIMS/uPBE0-D3 for 1st ps of alpha-terpinene photochemistry. 20 for each<br> conformer upon rotation around the isopropyl group. Each initial condition (IC) is saved in a<br> separate folder labeled after conformers m, p, t, and with the index number of the initial condition.<br> The folder contains the positions and amplitudes of all trajectory basis functions (TBF) arising from<br> the initial condition as well as information about their coupling. The TBF index 1 always refers to<br> the TBF launched in the Franck-Condon region of the excited state based on an initial condition<br> sampled from a ground state Wigner distribution. </p> <p>Description of the folders and subsequent files in each IC folder:</p> <p>Simulation_data <br> X-YYYY : X is the isomer and YYYY is the initial condition number<br> "Positions.x.xyz" : Files containing the geometries of each TBF in cartesian coordinates in Angstroms<br> at each time step. The "x" in the filename corresponds to the index of the TBF.<br> "Amp.x" : Files containing the TBF amplitudes for each timestep.<br> "Spawn.log" : File containing timing information about the spawning events.<br> "S.dat" : File containing coupling matrices between the TBFs for every time step.<br> "ext_x" : Folders containing extensions of TBFs on DFT level. Each folder contains a file<br> "coors.xyz" with cartesian coordinates in Angstroms at each time step. The<br> timestep size of the DFT trajectories is uniformly 0.5 femtoseconds. Only TBFs in<br> the groundstate are extended on DFT level. Therefore, there is no folder "ext_1".</p> <p>Simulation_Parameters : Parameters for FMS and TeraChem nonadiabatic dynamics <br> X-rotamer : x is isomer containing parameter files for all X rotamers<br> "c0.casscf" : Binary file with the alpha(0.82)-SA2-CAS(6,4)-SCF/6-31G* orbitals<br> "Geometry.dat" : Initial condition (position and momentum) to start the FMS/TeraChem nonadiabatic dynamics<br> "Control.dat" : Parameter file for running FMS<br> "misc_options" : Parameter file for running TeraChem <br> "DFT-tc.in" : TeraChem adiabatic dynamics on ground electronic state (ext_x)<br> </p>
SRDTrans dataset: simulated calcium imaging data sampled at 30 Hz under different SNRs
<p>SRDTrans dataset: simulated calcium imaging data sampled at 30 Hz under different SNRs.</p>
Simulating Outcomes From Breast Conserving Surgery Using 3D Surface Imaging
ClinicalTrials.gov study NCT03250260. IPD Sharing: NO. Countries: 1. Publications: 1.
Expectation maximization based framework for joint localization and parameter estimation in single particle tracking from segmented images - Simulation Data
Open the record for dataset details and reuse information.
Patient Specific Virtual Reality for Simulation of Spine Procedures: an Intelligent Image Segmentation, Registration and 3-dimensional Visualization in a Unified Virtual Reality Workflow for Image Gui
ClinicalTrials.gov study NCT06714539. IPD Sharing: UNDECIDED. Countries: 0. Publications: 1.
Data from: Simulation assisted analysis of the intrinsic stiffness for short DNA molecules imaged with scanning atomic force microscopy
Open the record for dataset details and reuse information.
Data from: Rapid quantitative pharmacodynamic imaging by a novel method: theory, simulation testing and proof of principle
Open the record for dataset details and reuse information.
CLAMS ER-2 Moderate Resolution Imaging Spectrometer (MODIS) Airborne Simulator (MAS)
CLAMS_ER2_MAS data were collected during the Chesapeake Lighthouse and Aircraft Measurements for Satellites (CLAMS) experiment with the objective to combine long term ocean spectral surface observations with satellite and aircraft measurements to enhance our knowledge of ocean surface reflections and aerosols.The MODIS Airborne Simulator (MAS) is an airborne scanning spectrometer that acquires high spatial resolution imagery of cloud and surface features from its vantage point on-board a NASA ER-2 high-altitude research aircraft. Data acquired by the MAS help to define, develop, and test algorithms for the Moderate Resolution Imaging Spectroradiometer (MODIS), a key sensor of NASA's Earth Observing System (EOS). The MODIS program emphasizes the use of remotely sensed data to monitor variation in environmental conditions for assessing both natural and human-induced global change.The MAS spectrometer acquires high spatial resolution imagery in the range of 0.55 to 14.3 microns. A total of 50 spectral bands are available in this range. Pre-1995 the digitizer was configured for each mission to record a pre-selected group of 12 bands during the flight. For most of these missions the digitizer was configured to record four 10-bit channels and seven 8-bit channels. A 50-channel digitizer which records all 50 spectral bands at 12 bit resolution became operational in January 1995.The MAS spectrometer is mated to a scanner sub-assembly which collects image data with an IFOV of 2.5 mrad, giving a ground resolution of 50 meters from 20000 meters altitude, and a cross track scan width of 85.92 degrees.
Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation
<p>A highly granular calorimeter, similar to CALICE is simulated in Geant4. The calorimeters showers are represented as either images or point clouds. Two state-of-the-art score based diffusion models, on image based and the other point cloud based, are trained on the same set of calorimeter simulations and directly compared to each other.<br> <br> These files include the original Geant4 simulation, an intermediate form of the data required for training, and the samples generated by the image and point cloud models.</p>
Magnetic resonance imaging of water vapor absorption in simulated corrugated board
<p>This dataset contains all the raw source data that comprise the study:</p> <p><strong>Magnetic resonance imaging of water vapor absorption in simulated corrugated board </strong></p> <p>Tuomainen TV (1), Silvennoinen J (2), Mattila T (2), Kettunen MI (3), Nissi MJ (1)*</p> <p>Cellulose | DOI: 10.1007/s10570-023-05483-3</p> <p> </p> <p>1: University of Eastern Finland, Department of Technical Physics, Yliopistonranta 8, FI-70211, Kuopio, Finland</p> <p>2: Mondi Powerflute, Selluntie 142, FI-70420, Kuopio Finland</p> <p>3: University of Eastern Finland, A.I. Virtanen Institute for Molecular Sciences, Kuopio Biomedical Imaging Unit, Neulaniementie 2, FI-70150, Kuopio, Finland</p> <p> </p> <p>*Corresponding author: </p> <p>Mikko J. Nissi </p> <p>Department of Technical Physics, UEF </p> <p>POB 1627 </p> <p>FI-70211, Kuopio, Finland </p> <p>mikko.nissi [at] uef.fi </p> <p>+358-50-5955517</p> <p> </p> <p><strong>Study and data description</strong></p> <p>The purpose of this work was to investigate water vapor permeation through a layered structure composing of liner and fluting paperboard materials. The layered structure with paperboards separated with ring spacers was used to simulate the structure of a double-wall cardboard.</p> <p>This dataset contains MRI measurements (RARE pulse sequence) of simulated cardboard assemblies with different fluting materials (Fluting A,B,C) under high humidity. Furthermore, temperature and relative humidity data for possible pre-treatment (in .MAT files) are included.</p> <p>Included folders and files in the <em>paperboard_zenodo_repository.zip</em> are:</p> <ul> <li><strong>prep_paperboard</strong>: Temperature and relative humidity of the pre-treatment for simulated cardboard as .MAT files (can be opened with MATLAB). Two ,MAT-files are included: '14102021_paperboard_flutingCa_nacl_mgcl_RH_t.mat' and '09122021_paperboard_nacl_flutingAB_RH_t.mat'. In the .MAT file regarding Fluting A and Fluting B pre-treated with NaCl (09122021_paperboard_nacl_flutingAB_RH_t.mat) Fluting A is designated as x1NaCl and Fluting B as x2NaCl.</li> <li><strong>MRI_paperboard</strong>: Contains the MRI data as 'fid' and '2dseq' as well as metadata for each experiment obtained at 11.7 T (Bruker)</li> <li><strong>README.txt: </strong>More information on the file and folder structure and datatypes.</li> </ul> <p> </p> <p>Further information is provided in the included <strong>README.txt</strong>.</p>
Daily Imaging, Target Identification, and Simulated Computed Tomography-Based Stereotactic Adaptive Radiotherapy Workflow in a Novel Ring Gantry Radiotherapy Device
ClinicalTrials.gov study NCT04008537. IPD Sharing: NO. Countries: 1. Publications: 0.
Survey of Current Interventional and Diagnostic Imaging Residents After Participation in Endovascular Simulator Training
ClinicalTrials.gov study NCT04832698. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Simulated Screening Study for Breast Imaging
ClinicalTrials.gov study NCT01807754. IPD Sharing: NO. Countries: 1. Publications: 0.
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