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105 results for “image calibration”
Mars 2020 Perseverance SHERLOC WATSON camera pre-delivery characterization and calibration image data
<p>The data presented here include images acquired by the WATSON (Wide Angle Topographic Sensor for Operations and eNgineering) camera during pre-delivery characterization and calibration testing at Malin Space Science Systems (MSSS, San Diego, California, USA) in September and October 2019. They also include video documentation of the camera’s dust cover motion. WATSON is one of two imaging subsystems of the SHERLOC (Scanning Habitable Environments with Raman & Luminescence for Organics & Chemicals) instrument onboard NASA’s Mars 2020 Perseverance rover which landed in Jezero crater, Mars, in February 2021.</p> <p>These data accompany the instrument calibration and characterization report by Edgett et al. (2019) and the WATSON characteristics reported by Bhartia et al. (2021). The image data presented here are listed and described in the Appendix to Edgett et al. (2019), which is also available here with the data.</p> <p>References cited:</p> <p>Bhartia, R., L. W. Beegle, L. DeFlores, W. Abbey, J. Razzell Hollis, K. Uckert, B. Monacelli, K. S. Edgett, M. R. Kennedy, M. Sylvia, D. Aldrich, M. Anderson, S. A. Asher, Z. Bailey, K. Boyd, A. S. Burton, M. Caffrey, M. J. Calaway, R. Calvet, B. Cameron, M. A. Caplinger, B. L. Carrier, N. Chen, A. Chen, M. J. Clark, S. Clegg, P. G. Conrad, M. Cooper, K. N. Davis, B. Ehlmann, L. Facto, M. D. Fries, D. H. Garrison, D. Gasway, F. T. Ghaemi, T. G. Graff, K. P. Hand, C. Harris, J. D. Hein, N. Heinz, H. Herzog, E. Hochberg, A. Houck, W. F. Hug, E. H. Jensen, L. C. Kah, J. Kennedy, R. Krylo, J. Lam, M. Lindeman, J. McGlown, J. Michel, E. Miller, Z. Mills, M. E. Minitti, F. Mok, J. Moore, K. H. Nealson, A. Nelson, R. Newell, B. E. Nixon, D. A. Nordman, D. Nuding, S. Orellana, M. Pauken, G. Peterson, R. Pollock, H. Quinn, C. Quinto, M. A. Ravine, R. D. Reid, J. Riendeau, A. J. Ross, J. Sackos, J. A. Schaffner, M. Schwochert, M. O Shelton, R. Simon, C. L. Smith, P. Sobron, K. Steadman, A. Steele, D. Thiessen, V. D. Tran, T. Tsai, M. Tuite, E. Tung, R. Wehbe, R. Weinberg, R. H. Weiner, R. C. Wiens, K. Williford, C. Wollonciej, Y.-H. Wu, R. A. Yingst, J. Zan (2021) Perseverance’s Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) investigation, Space Science Reviews 217, 58. https://doi.org/10.1007/s11214-021-00812-z</p> <p>Edgett, K. S., M. A. Caplinger, M. A. Ravine (2019) Mars 2020 Perseverance SHERLOC WATSON Camera Pre-delivery Characterization and Calibration Report, Malin Space Science Systems, San Diego, California. https://doi.org/10.13140/RG.2.2.18447.00165</p>
Feature Re-calibration based Multiple Instance Learning for Whole Slide Image Classification - Features
<p>A release of the sources employed in the FRMIL work - "Feature Re-calibration based Multiple Instance Learning for Whole Slide Image Classification", presented at MICCAI 2022.</p> <p>We release the Camelyon16 Whole Slide Image (WSI) extracted patch-level features for reproducibility.</p>
Organizing Structural Principles of the Interleukin-17 Ligand-Receptor Axis - Single molecule tracking - raw data - calibration images
<p>This dataset contains the images used for channel calibration for the single molecule data that was analyzed in the manuscript "Organizing Structural Principles of the Interleukin-17 Ligand-Receptor Axis"</p>
Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Standard calibration
<p>Accompanying data to publication: <strong>Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging</strong></p> <p>Hyperspectral data with all their processing steps - normalization, ROI, subsets, masking - resulting in RABD indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.</p>
Automated, high-throughput image calibration for parallel-laser photogrammetry
<p>This contains the data required to recreate the analyses in this paper. The code for performing the machine learning and image processing methods presented in the paper are available as supplemental files to the manuscript, and are also available at https://github.com/ejlevy/Photogrammetry_Coding_InterLaser_Distance.</p> <p>Paper abstract: <span>Parallel-laser photogrammetry is growing in popularity as a way to collect non-invasive body size data from wild mammals. Despite its many appeals, this method requires researchers to hand-measure (i) the pixel distance between the parallel laser spots (inter-laser distance) to produce a scale within the image, and (ii) the pixel distance between the study subject's body landmarks (inter-landmark distance). This manual effort is time-consuming and introduces human error: a researcher measuring the same image twice will rarely return the same values both times (resulting in within-observer error), as is the case when two researchers measure the same image (resulting in between-observer error). Here, we present two independent methods that automate the inter-laser distance measurement of parallel-laser photogrammetry images. One method uses machine learning and image processing techniques in Python, and the other uses image processing techniques in ImageJ. Both of these methods reduce labor and increase precision without sacrificing accuracy. We first introduce the workflow of the two methods. Then, using two parallel-laser datasets of wild mountain gorilla and wild savannah baboon images, we validate the precision of these two automated methods relative to manual measurements and to each other. We also estimate the reduction of variation in final body size estimates in centimeters when adopting these automated methods, as these methods have no human error. Finally, we highlight the strengths of each method, suggest best practices for adopting either of them, and propose future directions for the automation of parallel-laser photogrammetry data. </span></p>
UTC STRIDE Project G2: Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation
<p>This repository contains the extracted traffic data used in the case study for UTC STRIDE project G2 "Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation". The traffic data was extracted using manual or AI-based methods (presented in the project final report) from two 30-minute videos with high and low traffic density. </p> <p>Below are the description of each data file:</p> <ul> <li><strong>high_density_both_lane_raw_AI_speed.csv</strong> <ul> <li><strong>Description: </strong>extracted individual vehicle speed data of the high traffic density video using the AI-based method.</li> <li><strong>Data Fields:</strong> <ul> <li>object_id: unique id for each detected and tracked vehicle</li> <li>frame_index: frame index of the video when other tracked vehicle exit the virtual speed loop.</li> <li>lane_id: lane id (1=outer lane, 2=inner lane)</li> <li>speed: average speed traveling through the virtual speed loop (kph)</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_AI_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>AI extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the inner lane of the high traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_AI_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>AI extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the outer lane of the high traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>low_density_inner_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the inner lane of the low traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>low_density_inner_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the inner lane of the low traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>low_density_outer_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the outer lane of the low traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>low_density_outer_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the outer lane of the low traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> </ul>
Testing and Calibration of Non-Invasive Optical Imaging Technology for Functional Brain Imaging
ClinicalTrials.gov study NCT01212029. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Automated, high-throughput image calibration for parallel-laser photogrammetry
Open the record for dataset details and reuse information.
PASTA-ice sea ice image classification: calibration files and training data
<p>PASTA-ice is a Python-based classification algorithm for aerial sea-ice images. The data set contains calibration files for the CANON EOS-1D Mark III cameras that were implemented in helicopters and POLAR aircraft of the Alfred-Wegener-Institute. Furthermore, it contains training data of labeled sea ice surfaces observed during RV Polarstern cruise PS106 that can be used to train the implemented random forest classifier. Files with extension "sediments" were extended with exemplary data of sediment-loaden snow at the MOSAiC expedition. </p> <p>The PASTA-ice algorithm is available under: <a href="https://github.com/nielsfuchs/pasta_ice">https://github.com/nielsfuchs/pasta_ice</a></p>
AWI CANON Sea ice image albedo retrieval calibration files
<p>The dataset contains calibration data, compiled for a Python based retrieval of a broadband albedo estimate from CANON EOS-1D Mark III cameras deployed as aerial imaging system over Arctic sea ice. </p> <p>It contains information on the relative spectral response function of the camera sensor (RSR_CANON), calibration coefficients to convert spectral albedo in the three RGB color bands into broadband albedo under different sky conditions (spec2broad_params) and calibration coefficients to convert RGB intensity into radiance dependent on exposure settings (Calib_coefficients_14mm_diff_settings).</p>
2023/02/22 SCExAO/VAMPIRES Polarization Calibration Difference Images
<p>Polarization calibration data taken on 2023/02/22 (UT) for SCExAO/VAMPIRES. Data have been processed using the VAMPIRES Data Processing Pipeline (Lucas et al,, 2023 in prep). Included README describes experiment design and data outputs.</p>
2023/06/02 SCExAO/VAMPIRES Polarization Calibration Difference Images
<p>Polarization calibration data taken on 2023/06/02 (UT) for SCExAO/VAMPIRES. Data have been processed using the VAMPIRES Data Processing Pipeline (Lucas et al,, 2023 in prep; <a href="http://github.com/scexao-org/vampires_dpp">link</a>). Included README describes experiment design and data outputs.</p>
Calibrating Imaging Techniques to Study Blood Flow
ClinicalTrials.gov study NCT00546091. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Data from: Image Calibration and Analysis Toolbox – a free software suite for measuring reflectance, colour, and pattern objectively and to animal vision
1. Quantitative measurements of colour, pattern, and morphology are vital to a growing range of disciplines. Digital cameras are readily available and already widely used for making these measurements, having numerous advantages over other techniques, such as spectrometry. However, off-the-shelf consumer cameras are designed to produce images for human viewing, meaning that their uncalibrated photographs cannot be used for making reliable, quantitative measurements. Many studies still fail to appreciate this, and of those scientists who are aware of such issues, many are hindered by a lack usable tools for making objective measurements from photographs. 2. We have developed an image processing toolbox that generates images that are linear with respect to radiance from the RAW files of numerous camera brands, and can combine image channels from multispectral cameras, including additional ultraviolet photographs. Images are then normalised using one or more grey standards to control for lighting conditions. This enables objective measures of reflectance and colour using a wide range of consumer cameras. Furthermore, if the camera's spectral sensitivities are known, the software can convert images to correspond to the visual system (cone-catch values) of a wide range of animals, enabling human and non-human visual systems to be modelled. The toolbox also provides image analysis tools that can extract luminance (lightness), colour, and pattern information. Furthermore, all processing is performed on 32-bit floating point images rather than commonly used 8-bit images. This increases precision and reduces the likelihood of data loss through rounding error or saturation of pixels, while also facilitating the measurement of objects with shiny or fluorescent properties. 3. All cameras tested using this software were found to demonstrate a linear response within each image and across a range of exposure times. Cone-catch mapping functions were highly robust, converting images to several animal visual systems and yielding data that agreed closely with spectrometer-based estimates. 4. Our imaging toolbox is freely available as an addition to the open source ImageJ software. We believe that it will considerably enhance the appropriate use of digital cameras across multiple areas of biology, in particular researchers aiming to quantify animal and plant visual signals.
GB GMOS gri-band coadded and fully calibrated FITS images
<p>These are the Gemini/GMOS-N and GMOS-S coadded FITS images, taken in gri filters, of the 17 Green bean galaxies published in Schirmer+ 2016.</p>
Data from: Image Calibration and Analysis Toolbox – a free software suite for measuring reflectance, colour, and pattern objectively and to animal vision
Open the record for dataset details and reuse information.
Use of Hyperpolarized 129Xe MR Lung Imaging in Adults for Calibration
ClinicalTrials.gov study NCT02316379. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Image-based Multi-scale Modeling Framework of the Cardiopulmonary System: Longitudinal Calibration and Assessment of Therapies in Pediatric Pulmonary Hypertension
ClinicalTrials.gov study NCT03564522. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Calibration of MR and PET-MR Imaging Protocols at RIC
ClinicalTrials.gov study NCT02249520. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Polar Visible Imaging System (VIS) Low Resolution Camera Images, Calibrated, Level 0 (L0), 12 s Data
Instrument Functional Description: The VIS Instrument is a Set of three Low Light Level Cameras. Two of these Cameras share primary and some secondary Optics and are designed to provide Images of the Nighttime Auroral Oval at Visible Wavelengths. A Third Camera is used to monitor the Directions of the Fields-of-View of the Auroral Cameras with respect to the sunlit Earth and return Global Images of the Auroral Oval at Ultraviolet Wavelengths. The VIS Instrumentation produces an Auroral Image of 256 × 256 Pixels approximately every 24 s dependent on the Integration Time and Filter selected. The Fields-of-View of the two Nighttime Auroral Cameras are 5.6 × 6.3° and 2.8 × 3.3° for the Low and Medium Resolution Cameras, respectively. The Medium Resolution Camera was never activated. One or more Earth Camera Images of 256 × 256 Pixels are produced every 5 min, depending on the commanded Mode. The Field-of-View of the Earth Camera is approximately 20 × 20°. See: http://vis.physics.uiowa.edu/vis/vis_description/vis_description.htmlx Reference: Frank, L.A., J.B. Sigwarth, J.D. Craven, J.P. Cravens, J.S. Dolan, M.R. Dvorsky, J.D. Harvey, P.K. Hardebeck, and D. Muller, The Visible Imaging System (VIS) for the Polar Spacecraft, Space Science Review, Vol. 71, pp. 297-328, 1995. Data Set Description: The VIS Earth Camera Data Set comprises all Earth Camera Images for the selected Time Period. Full Coordinate Information is included for Viewer Orientation. In addition, a Rotation Matrix and a Table of Distortion-correcting Look Direction Unit Vectors are provided for the Purpose of calculating Coordinates for every Pixel. To facilitate viewing of the Images, a Mapping of Pixel Value to a recommended Color Table based on the Characteristics of the selected Filter will be included with each Image. A Relative Intensity Scale is provided through an Uncompressed Count Table. Approximate Intensity Levels in kiloRayleighs are given in an Intensity Table. For detailed Information on Intensities, see Sensitivities_and_Intensities.txt at https://cdaweb.gsfc.nasa.gov/Polar_VIS_docs/SENSITIVITIES_AND_INTENSITIES.TXT. Supporting Software is available at: http://vis.physics.uiowa.edu/vis/software/ Included is an IDL Program that displays the Images with the recommended Color Bar, provides approximate Intensities, Coordinate Data for each Pixel, and includes multiple Options for Image Manipulation.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.