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21 results for “specularity”
Cracks in the mirror hypothesis: high specularity does not reduce detection or predation risk
<p>Some animals, including certain fish, beetles, spiders and Lepidoptera chrysalises, have such shiny or glossy surfaces that they appear almost mirror-like. A compelling but unsubstantiated hypothesis is that a highly specular or mirror-like appearance enhances survival by reflecting the surrounding environment and reducing detectability.</p> <p>We tested this hypothesis by asking human participants to wear a mobile eye-tracking device and locate highly realistic mirror-green and diffuse-green replica beetles against a variety of backgrounds in a natural forest environment. We also tested whether a mirror-like appearance enhances survival to wild predators by monitoring survival of mirror-green and diffuse-green replica beetles in a forested habitat and an open habitat.</p> <p>Human participants showed no difference in the detection probability or detection latency of mirror versus diffuse replica beetles, indicating that mirror-like appearance does not impair prey capture. The field predation experiment found no difference in survival between the mirror and diffuse replica beetles in forested environments. Similarly, there was no difference in survival when beetles were deployed in open habitat where there is no background to reflect, indicating that predators detect and do not actively avoid mirror-like beetles.</p> <p>Our results suggest that a mirror-like appearance does not reduce attack by predators. Instead, highly specular, mirror-like surfaces may have evolved for an alternate visual function or as a secondary consequence of selection for a non-visual function, such as thermoregulation.</p>
Cracks in the mirror hypothesis: high specularity does not reduce detection or predation risk
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Specular Meteor Radar wind estimates from Tirupati, used in "Validation of ICON-MIGHTI thermospheric wind observations: 2. Greenline comparisons to specular meteor radars" by Harding et al. (2021)"
<pre>This dataset was used to generate the figures in the paper mentioned above and is being made available for the sake of reproducibility and future analysis. The primary variables are u0, v0 (the zonal and meridional wind profiles observed by the meteor radar). Dimensions are "time" and "alt" (in km). Velocity units are m/s, and lat/lon are in degrees. More information can be found in the paper. Please contact and get permission from the data providers (M. Venkat Ratnam and S. Vijaya Bhaskara Rao) before using the data in any publications or presentations.</pre>
Deer Stamp Normal Map RTI & Specular
I decided to try some photoshop filtering to get a loose specular map for this model. I think it turned out pretty nice. Normal map is from RTI method and the model was generated in Blender. As usual with RTI stuff, the matcap is definitely worth looking at. Scans were done by AISOS, a part of LATIS Labs in the College of Liberal Arts at the University of Minnesota, Twin Cities. Source: Objaverse 1.0 / Sketchfab
Bats actively use leaves as specular reflectors to detect acoustically camouflaged prey
<p>Measured target strength from 541 positions for 5 different frequency bands. Bat positions in incidence angles of for 33 flight paths.</p>
Anthropogenic Specular Interference in the Operational GOES-R Fire Product
<p><strong>Dataset for the research paper "Anthropogenic Specular Interference in the Operational GOES-R Fire Product".</strong></p> <p>Large reflective structures like solar power plants and commercial greenhouses sometimes reflect sunlight directly into GOES-R sensors. These anthropogenic specular reflections, or "sparkles", cause commission errors in operational GOES-R ABI products like the Fire Detection and Characterization Algorithm (FDCA). Using the <em>abi-sparkle</em> library for Python (Dove-Robinson, 2023), we generated a dataset containing both detected anthropogenic specular reflection pixels and the coincident FDCA commission errors caused by them for the GOES-16 CONUS domain during the 2020 calendar year.</p> <p>The dataset consists of two exported PostgreSQL tables: <em>sparkle_pixels_g16_abi_conus_2020</em>, which contains the detected anthropogenic specular reflection pixels at 500 m resolution, and <em>fdca_commission_error_clusters_g16_abi_conus_2020</em>, which contains clustered FDCA false alarm fire pixels caused by anthropogenic specular reflection at 2 km resolution. The FDCA pixels were only processed for fire mask codes 10-15 and 30-35; see Table 3.11 in Schmidt et al., 2013 for fire code definitions.</p> <p>Each row in <em>sparkle_pixels_g16_abi_conus_2020 </em>is a detected specular reflection pixel in a GOES-16 CONUS image from the 2020 calendar year with a unique numeric ID <em>sparkle_id </em>and associated metadata from the detection algorithm <em>abi-sparkle</em>. The column <em>sparkle_geom </em>is a PostGIS geometry ST_Point object that can be used to plot the pixels on a map.</p> <p>FDCA fire pixels at 2 km resolution were clustered based on their connectivity in a 3x3 pixel kernel and assigned a UUID <em>fire_cluster_id </em>in the table <em>fdca_commission_error_clusters_g16_abi_conus_2020</em>. Only the fire clusters that overlapped with sparkle pixels in time and space were retained in the table. In this way, each row of <em>fdca_commission_error_clusters_g16_abi_conus_2020 </em>is a unique cluster of errant FDCA fire pixels caused by anthropogenic specular reflection in every available scan start time for the GOES-16 CONUS domain in 2020. Every fire cluster centroid has a PostGIS geometry object <em>fire_cluster_centroid_geom </em>that can be used to plot the errant fire pixel clusters on a map.</p> <p>The two tables relate with the column <em>sparkle_ids </em>in <em>fdca_commission_error_clusters_g16_abi_conus_2020</em>, which is an array of overlapping sparkle IDs from the <em>sparkle_pixels_g16_abi_conus_2020 </em>table. The combined dataset may therefore be generated with a simple SQL INNER JOIN:</p> <p><em>SELECT * FROM fdca_commission_error_clusters_g16_abi_conus_2020 fcecgac<br> INNER JOIN sparkle_pixels_g16_abi_conus_2020 spgac ON spgac.sparkle_id = ANY(fcecgac.sparkle_ids);</em></p> <p>The tables can be imported into a PostgreSQL database version 12 or newer with PostGIS extensions installed. For example, to import the tables into a database in a Linux environment, run the following commands:</p> <p><em>gunzip -c sparkle_pixels_g16_abi_conus_2020.sql.gz | psql -d your_database_name<br> gunzip -c fdca_commission_error_clusters_g16_abi_conus_2020.sql.gz | psql -d your_database_name</em></p>
Specular Meteor Radar Observations of the Semidiurnal Tide in Northern Scandinavia and Northern Germany
<p>The dataset contains specular meteor radar observations of the atmospheric semidiurnal tide in the mesosphere and lower thermosphere. The observations are from specular meteor radars located in Northern Scandinavia (Andenes, Kiruna, and Tromso) and Northern Germany (Collm and Juliusruh). The radar observations in Northern Scandinavia cover the years 1999, 2000, 2001, 2002, 2010, 2012, 2013, 2015, and 2019. Observations in Northern Germany cover the years 2010, 2012, 2013, 2015, and 2019. These data are in support of the publication "Migrating Semidiurnal Tide during the September Equinox Transition in the Northern Hemisphere."</p>
CLTS-GAN: Color-Lighting-Texture-Specular Reflection Augmentation for Colonoscopy
<p>This is the models as well as results for CLTS-GAN, a deep learning model that disentangles color and lighting and texture and specular information. The results for the model as well as an augmented polyp dataset are contained here.</p> <p><strong>Abstract:</strong></p> <p>Automated analysis of optical colonoscopy (OC) video frames (to assist endoscopists during OC) is challenging due to variations in color, lighting, texture, and specular reflections. Previous methods either remove some of these variations via preprocessing (making pipelines cumbersome) or add diverse training data with annotations (but expensive and time-consuming). We present CLTS-GAN, a new deep learning model that gives fine control over color, lighting, texture, and specular reflection synthesis for OC video frames. We show that adding these colonoscopy-specific augmentations to the training data can improve state-of-the-art polyp detection/segmentation methods as well as drive next generation of OC simulators for training medical students. You can find the code and additional detail about CLTS-GAN via our Computation Endoscopy Platform at <a href="https://github.com/nadeemlab/CEP">https://github.com/nadeemlab/CEP</a></p>
Dataset From: Structuring of colloidal silica nanoparticle suspensions near water–silica interfaces probed by specular neutron reflectivity
<p>The dataset for the publication "Structuring of colloidal silica nanoparticle suspensions near water–silica interfaces probed by specular neutron reflectivity". DOI: 10.1039/d0cp00465k.</p> <p>Files containing data have .dat extension and are in text format.</p>
Comparative Study of Specular Microscopes for Measurements of Cell Density, Coefficient of Variation and Hexagonality
ClinicalTrials.gov study NCT02276638. IPD Sharing: NO. Countries: 1. Publications: 0.
Specular Microscopy in Patients With Proliferative Diabetic Retinopathy
ClinicalTrials.gov study NCT05838898. IPD Sharing: NO. Countries: 0. Publications: 2.
Corneal Specular Microscopy in Infectious and Noninfectious Uveitis
ClinicalTrials.gov study NCT00789984. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Specular Microscopy Study
ClinicalTrials.gov study NCT02478970. IPD Sharing: NO. Countries: 1. Publications: 0.
Measuring Corneal Cells With Specular Microscopy
ClinicalTrials.gov study NCT01792154. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Comparative Study of Specular Microscopes for Endothelial and Corneal Cell Measurements
ClinicalTrials.gov study NCT02138266. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effect of Epi-Off Technique Corneal CXL On Endothelial Count by Specular Microscopy in Keratoconus Patients
ClinicalTrials.gov study NCT05717673. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Comparative Study of Specular Microscope for Cell Density, Coefficient of Variation, Hexagonality and Corneal Thickness
ClinicalTrials.gov study NCT01729975. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Second Comparative Study of Specular Microscopes for Endothelial and Corneal Cell Measurements
ClinicalTrials.gov study NCT02427256. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluation of the Corneal Endothelial Cell Layer by Specular Microscopy in Patients With Thyroid Eye Disease
ClinicalTrials.gov study NCT06235372. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Comparative Study of the TOMEY Specular Microscope EM-4000 and the Konan CellChek XL
ClinicalTrials.gov study NCT02977793. IPD Sharing: NO. Countries: 0. Publications: 0.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.