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981 results for “ear”
Figure 1 in The braincase and middle ear region of Dendrerpeton acadianum (Tetrapoda: Temnospondyli)
Figure 1. Main cranial block of BMNH R.436 placed in approximate position of a full cranial representation of Dendrerpeton (after Carroll, 1967) for orientation and appreciation of the incomplete nature of the specimen. A, dorsal view. B, ventral view.
Figure 3. A in The braincase and middle ear region of Dendrerpeton acadianum (Tetrapoda: Temnospondyli)
Figure 3. A, frontal CT section with airfill perimeter shown with dashed line. B, posterior view of airfill. Scale bars = 2 mm.
Figure 2 in The braincase and middle ear region of Dendrerpeton acadianum (Tetrapoda: Temnospondyli)
Figure 2. Medial view of left pterygoid. A, remaining bone. B, remaining bone and internal mould. Bone in lighter shading, internal mould in darker shading. Scale bars = 2 mm.
Data and code for: Roost selection by male northern long-eared bats (Myotis septentrionalis) in a managed fire-adapted forest
<p>Data and code for: Roost selection by male northern long-eared bats (<em>Myotis septentrionalis</em>) in a managed fire-adapted forest</p>
Data for: Predicting habitat suitability for Townsend's big-eared bats across California in relation to climate change
<p>Aim: Effective management decisions depend on knowledge of species distribution and habitat use. Maps generated from species distribution models are important in predicting previously unknown occurrences of protected species. However, if populations are seasonally dynamic or locally adapted, failing to consider population level differences could lead to erroneous determinations of occurrence probability and ineffective management. The study goal was to model the distribution of a species of special concern, Townsend's big-eared bats (Corynorhinus townsendii), in California. We incorporate seasonal and spatial differences to estimate the distribution under current and future climate conditions.</p> <p>Methods: We built species distribution models using all records from statewide roost surveys and by subsetting data to seasonal colonies, representing different phenological stages, and to Environmental Protection Agency Level III Ecoregions to understand how environmental needs vary based on these factors. We projected species' distribution for 2061-2080 in response to low and high emissions scenarios and calculated the expected range shifts.</p> <p>Results: The estimated distribution differed between the combined (full dataset) and phenologically-explicit models, while ecoregion-specific models were largely congruent with the combined model. Across the majority of models, precipitation was the most important variable predicting the presence of C. townsendii roosts. Under future climate scnearios, distribution of C. townsendii is expected to contract throughout the state, however suitable areas will expand within some ecoregions. Main conclusion: Comparison of phenologically-explicit models with combined models indicate the combined models better predict the extent of the known range of C. townsendii in California. However, life history-explicit models aid in understanding of different environmental needs and distribution of their major phenological stages. Differences between ecoregion-specific and statewide predictions of habitat contractions highlight the need to consider regional variation when forecasting species' responses to climate change. These models can aid in directing seasonally explicit surveys and predicting regions most vulnerable under future climate conditions.</p>
AudioEar: Single-View Ear Reconstruction for Personalized Spatial Audio
<p> </p> <p><strong>Abstract</strong></p> <p>We introduce AudioEar3D, a high-quality 3D ear dataset consisting of 112 point cloud ear scans with RGB images, to benchmark the ear reconstruction task. We further collect a 2D ear dataset composed of 2,000 images, each one with manual annotation of occlusion and 55 landmarks, named AudioEar2D. To our knowledge, both datasets have the largest scale and best quality of their kinds for public use.</p> <p>The code is publicly available at <a href="https://medmnist.com/">https://github.com/seanywang0408/AudioEar</a>.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you find this project useful, currently please cite the paper as:</p> <blockquote> <pre>Xiaoyang Huang, Yanjun Wang, Yang Liu, Bingbing Ni, Wenjun Zhang, Jinxian Liu, Teng Li. "AudioEar: Single-View Ear Reconstruction for Personalized Spatial Audio". arXiv preprint arXiv:2301.12613, 2023.</pre> </blockquote> <p>or using bibtex:</p> <blockquote> <pre>@article{huang2023audioear, title={AudioEar: Single-View Ear Reconstruction for Personalized Spatial Audio}, author={Huang, Xiaoyang and Wang, Yanjun and Liu, Yang and Ni, Bingbing and Zhang Wenjun and Liu Jinxian and Li, Teng}, journal={arXiv preprint arXiv:2301.12613}, year={2023} }</pre> </blockquote> <p> </p> <p><strong>License</strong></p> <p>The dataset is licensed under <em>Creative Commons Attribution 4.0 International</em> (<a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a>). </p> <p>The code is under <a href="https://github.com/MedMNIST/MedMNIST/blob/main/LICENSE">Apache-2.0 License</a>.</p> <p> </p> <p><strong>Mirror Link</strong></p> <p>We recommend users to download the data from Zenodo official link. However, if you find any downloading problem, you can also use this mirror link from <a href="https://drive.google.com/drive/folders/1fWTtaFVkEAgLQRz55h8jQt7eJ6omFjxK?usp=sharing">Google Drive</a>.</p> <p> </p> <p><strong>Changelog</strong></p> <p><a href="https://doi.org/10.5281/zenodo.7592895">v1.1</a>: Add README.md.</p> <p><a href="https://doi.org/10.5281/zenodo.7581758">v1.0</a>: Initial repository of AudioEar3D and AudioEar2D.</p> <p> </p>
An Occlusion and Pose Sensitive Image Dataset for Black Ear Recognition
<p><strong>RESEARCH APPROACH</strong></p> <p>The research approach adopted for the study consists of seven phases which includes as shown in Figure 1:</p> <ol> <li>Pre-acquisition</li> <li>data pre-processing</li> <li>Raw images collection</li> <li>Image pre-processing</li> <li>Naming of images</li> <li>Dataset Repository</li> <li>Performance Evaluation</li> </ol> <p>The different phases in the study are discussed in the sections below.</p> <p> </p> <p><strong>PRE-ACQUISITION</strong></p> <p>The volunteers are given brief orientation on how their data will be managed and used for research purposes only. After the volunteers agrees, a consent form is given to be read and signed. The sample of the consent form filled by the volunteers is shown in Figure 1.</p> <p>The capturing of images was started with the setup of the imaging device. The camera is set up on a tripod stand in stationary position at the height 90 from the floor and distance 20cm from the subject.</p> <p> </p> <p><strong>EAR </strong><strong>IMAGE ACQUISITION</strong></p> <p>Image acquisition is an action of retrieving image from an external source for further processing. The image acquisition is purely a hardware dependent process by capturing unprocessed images of the volunteers using a professional camera. This was acquired through a subject posing in front of the camera. It is also a process through which digital representation of a scene can be obtained. This representation is known as an image and its elements are called pixels (picture elements). The imaging sensor/camera used in this study is a Canon E0S 60D professional camera which is placed at a distance of 3 feet form the subject and 20m from the ground. </p> <p>This is the first step in this project to achieve the project’s aim of developing an occlusion and pose sensitive image dataset for black ear recognition. (OPIB ear dataset). To achieve the objectives of this study, a set of black ear images were collected mostly from undergraduate students at a public University in Nigeria.</p> <p> </p> <p>The image dataset required is captured in two scenarios:</p> <p>1. uncontrolled environment with a surveillance camera</p> <ol> </ol> <p>The image dataset captured is purely black ear with partial occlusion in a constrained and unconstrained environment.</p> <p> </p> <p>2. controlled environment with professional cameras</p> <p>The ear images captured were from black subjects in controlled environment. To make the OPIB dataset pose invariant, the volunteers stand on a marked positions on the floor indicating the angles at which the imaging sensor was captured the volunteers’ ear. The capturing of the images in this category requires that the subject stand and rotates in the following angles 60<sup>o</sup>, 30<sup>o</sup> and 0<sup>o</sup> towards their right side to capture the left ear and then towards the left to capture the right ear (Fernando <em>et al.,</em> 2017) as shown in Figure 4. Six (6) images were captured per subject at angles 60<sup>o</sup>, 30<sup>o</sup> and 0<sup>o</sup> for the left and right ears of 152 volunteers making a total of 907 images <strong><em>(five volunteers had 5 images instead of 6, hence f</em></strong><strong><em>olders 34, 22, 51, 99 and 102 contain 5 images).</em></strong></p> <p>To make the OPIB dataset occlusion and pose sensitive, partial occlusion of the subject’s ears were simulated using rings, hearing aid, scarf, earphone/ear pods, etc. before the images are captured.</p> <p> </p> <table> <tbody> <tr> <td> <p><strong>CONSENT FORM</strong></p> <p>This form was designed to obtain participant’s consent on the project titled: <strong>An Occlusion and Pose Sensitive Image Dataset for Black Ear Recognition</strong><strong> (OPIB)</strong>. The information is purely needed for academic research purposes and the ear images collected will curated anonymously and the identity of the volunteers will not be shared with anyone. The images will be uploaded on online repository to aid research in ear biometrics.</p> <p>The participation is voluntary, and the participant can withdraw from the project any time before the final dataset is curated and warehoused.</p> <p>Kindly sign the form to signify your consent.</p> <p><strong><em>I consent to my image being recorded in form of still images or video surveillance as part of the OPIB ear images project.</em></strong></p> <p><strong>Tick as appropriate:</strong></p> <p><strong>GENDER</strong> Male Female</p> <p><strong>AGE</strong> (18-25) (26-35) (36-50)</p> <p> </p> <p>………………………………..</p> <p>SIGNED</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Figure 1</strong>: Sample of Subject’s Consent Form for the OPIB ear dataset</p> <p> </p> <p><strong>RAW IMAGE COLLECTION</strong></p> <p>The ear images were captured using a digital camera which was set to JPEG because if the camera format is set to raw, no processing will be applied, hence the stored file will contain more tonal and colour data. However, if set to JPEG, the image data will be processed, compressed and stored in the appropriate folders.</p> <p> </p> <p><strong>IMAGE PRE-PROCESSING </strong></p> <p>The aim of pre-processing is to improve the quality of the images with regards to contrast, brightness and other metrics. It also includes operations such as: cropping, resizing, rescaling, etc. which are important aspect of image analysis aimed at dimensionality reduction. The images are downloaded on a laptop for processing using MATLAB.</p> <p> </p> <p><strong>Image Cropping</strong></p> <p>The first step in image pre-processing is image cropping. Some irrelevant parts of the image can be removed, and the image Region of Interest (ROI) is focused. This tool provides a user with the size information of the cropped image. MATLAB function for image cropping realizes this operation interactively by waiting for a user to specify the crop rectangle with the mouse and operate on the current axes. The output images of the cropping process are of the same class as the input image.</p> <p><strong>Naming of OPIB Ear Images</strong></p> <p>The OPIB ear images were labelled based on the naming convention formulated from this study as shown in Figure 5. The images are given unique names that specifies the subject, the side of the ear (left or right) and the angle of capture. The first and second letters (SU) in the image names is block letter simply representing subject for subject 1-to-n in the dataset, while the left and right ears is distinguished using L1, L2, L3 and R1, R2, R3 for angles 60<sup>0</sup>, 30<sup>0</sup> and 0<sup>0</sup><sub>, </sub>respectively as shown in Table 1.</p> <p> </p> <p><strong>Table 1: Naming Convention for OPIB ear images</strong></p> <table align="center"> <tbody> <tr> <td> <p>NAMING CONVENTION</p> </td> </tr> <tr> <td> <p>Label</p> <p>Degrees 60<sup>0</sup> 30<sup>0</sup> 0<sup>0</sup></p> </td> </tr> <tr> <td> <p>No of the degree 1 2 3</p> </td> </tr> <tr> <td> <p>Subject 1 indicates (first image in dataset) SU<sub>1</sub></p> </td> </tr> <tr> <td> <p>Subject n indicates (last image in dataset) SU<sub>n</sub></p> </td> </tr> <tr> <td> <p>Left Image 1 L 1</p> <p>Left image n L n</p> <p>Right Image 1 R 1</p> <p>Right Image n R n</p> </td> </tr> <tr> <td> <p>SU1L<sub>1</sub> SU1R<sub>I</sub></p> <p>SU1L<sub>2</sub> SU1R<sub>2</sub> </p> <p>SU1L<sub>3</sub> SU1R<sub>3</sub></p> </td> </tr> </tbody> </table> <p> </p> <p><strong>OPIB EAR DATASET EVALUATION</strong></p> <p>The prominent challenges with the current evaluation practices in the field of ear biometrics are the use of different databases, different evaluation matrices, different classifiers that mask the feature extraction performance and the time spent developing framework (Abaza <em>et al.</em>, 2013; Emeršič <em>et al.,</em> 2017).</p> <p>The toolbox provides environment in which the evaluation of methods for person recognition based on ear biometric data is simplified. It executes all the dataset reads and classification based on ear descriptors.</p> <p> </p> <p><strong>DESCRIPTION OF OPIB EAR DATASET</strong></p> <p>OPIB ear dataset was organised into a structure with each folder containing 6 images of the same person. The images were captured with both left and right ear at angle 0, 30 and 60 degrees. The images were occluded with earing, scarves and headphone etc. The collection of the dataset was done both indoor and outdoor. The dataset was gathered through the student at a public university in Nigeria. The percentage of female (40.35%) while Male (59.65%). The ear dataset was captured through a profession camera Nikon D 350. It was set-up with a camera stand where an individual captured in a process order. A total number of 907 images was gathered.</p> <p>The challenges encountered in term of gathering students for capturing, processing of the images and annotations. The volunteers were given a brief orientation on what their ear could be used for before, it was captured, for processing. It was a great task in arranging the ear (dataset) into folders and naming accordingly.</p> <p> </p> <p><strong>Table 2</strong>: Overview of the OPIB Ear Dataset</p> <table align="left"> <tbody> <tr> <td> <p>Location</p> </td> <td> <p>Both Indoor and outdoor environment</p> </td> </tr> <tr> <td> <p>Information about Volunteers</p> </td> <td> <p>Students</p> </td> </tr> <tr> <td> <p>Gender</p> </td> <td> <p>Female (40.35%) and male (59.65%)</p> </td> </tr> <tr> <td> <p>Head Side Left and Right</p> </td> <td> <p>Side Left and Right</p> </td> </tr> <tr> <td> <p>Total number of volunteers</p> </td> <td> <p>152</p> </td> </tr> <tr> <td> <p>Per Subject images</p> </td> <td> <p>3 images of left ear and 3 images of right ear</p> </td> </tr> <tr> <td> <p>Total Images</p> </td> <td> <p>907</p> </td> </tr> <tr> <td> <p>Age group</p> </td> <td> <p>18 to 35 years</p> </td> </tr> <tr> <td> <p>Colour Representation</p> </td> <td> <p>RGB</p> </td> </tr> <tr> <td> <p>Image Resolution</p> </td> <td> <p>224x224</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Dataset of acoustic intensity vector measurements around an upscaled ear model
<p>A dataset of acoustic vector (particle velocity vector and scalar sound pressure) measurements of the sound field around an upscaled model of an ear. Data collected in July 2022 at the Aalto Acoustics Lab in Espoo, Finland.</p> <p>See the companion paper at AES for information about the contents of the dataset, measurement methodology, and example scripts.</p> <p>See the companion repository <a href="https://github.com/aaron-geldert/upscaled-ear-model-scripts">github.com/aaron-geldert/upscaled-ear-model-scripts</a> for example MATLAB scripts using the dataset.</p> <p>Correspondence should be directed to <a href="mailto:aarongeldert@gmail.com?subject=RE%20Big%20Ear%20Dataset%20(Zenodo)">Aaron Geldert (aarongeldert@gmail.com)</a>. <br> </p>
Data: "Happy new ears: rapid adaptation to novel spectral cues in vertical sound localization."
<p>Data related to the manuscript entitled: "Happy new ears: rapid adaptation to novel spectral cues in vertical sound localization.". For more information, read the README file.</p>
Examples of PET tracers synthesized via arene C-H radiofluorination (A) Maximum intensity projection (MIP) PET images of [18F]Fenoprofen (42) demonstrate higher uptake in TPA-treated mouse ear
<p>Examples of PET tracers synthesized via arene C-H radiofluorination (A) Maximum intensity projection (MIP) PET images of [18F]Fenoprofen (42) demonstrate higher uptake in TPA-treated mouse ear (A-1) compared with control (A-2) mouse ear. (B) PET/CT images demonstrate preferential tumor (MCF-7) accumulation of 39, compared with longer blood circulation and higher non-specific binding of 41 at 1 hour post-injection. (C) Structures of the tracers used in the preceding panels are shown.</p>
Data from: Development and application of a novel approach to scoring ear tag wounds in dairy calves
Open the record for dataset details and reuse information.
Data for: Predicting habitat suitability for Townsend’s big-eared bats across California in relation to climate change
Open the record for dataset details and reuse information.
Healing progression of tail docking and ear tag wounds in lambs
Open the record for dataset details and reuse information.
Ecological niche models for American black bear, Rafinesque's big-eared bat, and timber rattlesnake
Open the record for dataset details and reuse information.
Least-cost habitat linkages for American black bear, Rafinesque's big-eared bat, and timber rattlesnake.
Open the record for dataset details and reuse information.
FCA-estimated wind using EAR spaced antenna
<p>Dataset for horizontal wind estimation using EAR spaced antenna from configuration 1, 2, 3, 4, and 5.</p> <p>These are the output from FCA method. </p>
OpenEar color example from Visible Ear Simulator anatomy Delta
<p>This dataset is an example of the color data used in the non-drillable anatomy segments of the Visible Ear Simulator academic freeware.</p> <p>It contains UV maps for larger segments as well as 3D models including vertex colors.</p> <p>For a full description of the work related to this dataset, please refer to the published article in <em>Otology & Neurotology</em>.</p>
Data from: Non-invasive biophysical measurement of travelling waves in the insect inner ear
Frequency analysis in the mammalian cochlea depends on the propagation of frequency information in the form of a travelling wave (TW) across tonotopically arranged auditory sensilla. TWs have been directly observed in the basilar papilla of birds and the ears of bush-crickets (Insecta: Orthoptera) and have also been indirectly inferred in the hearing organs of some reptiles and frogs. Existing experimental approaches to measure TW function in tetrapods and bush-crickets are inherently invasive, compromising the fine-scale mechanics of each system. Located in the forelegs, the bush-cricket ear exhibits outer, middle and inner components; the inner ear containing tonotopically arranged auditory sensilla within a fluid-filled cavity, and externally protected by the leg cuticle. Here, we report bush-crickets with transparent ear cuticles as potential model species for direct, non-invasive measuring of TWs and tonotopy. Using laser Doppler vibrometry and spectroscopy, we show that increased transmittance of light through the ear cuticle allows for effective non-invasive measurements of TWs and frequency mapping. More transparent cuticles allow several properties of TWs to be precisely recovered and measured in vivo from intact specimens. Our approach provides an innovative, non-invasive alternative to measure the natural motion of the sensilla-bearing surface embedded in the intact inner ear fluid.
Data from: Comparative analysis of the shape and size of the middle ear cavity of turtles reveals no correlation with habitat ecology
<p>The middle ear of turtles differs from other reptiles in being separated into two distinct compartments. Several ideas have been proposed as to why the middle ear is compartmentalized in turtles, most suggesting a relationship with underwater hearing. Extant turtle species span fully marine to strictly terrestrial habitats, and ecomorphological hypotheses of turtle hearing predict that this should correlate with variation in the structure of the middle ear due to differences in the fluid properties of water and air. We investigate the shape and size of the air-filled middle ear cavity of 56 extant turtles using 3D data and phylogenetic comparative analysis to test for correlations between habitat preferences and the shape and size of the middle ear cavity. Only weak correlations are found between middle ear cavity size and ecology, with aquatic taxa having proportionally smaller cavity volumes. The middle ear cavity of turtles exhibits high shape diversity among species, but we found no relationship between this shape variation and ecology. Surprisingly, the estimated acoustic transformer ratio, a key functional parameter of impedance-matching ears in vertebrates, also shows no relation to habitat preferences (aquatic/terrestrial) in turtles. We suggest that middle ear cavity shape may be controlled by factors unrelated to hearing, such as the spatial demands of surrounding cranial structures. A review of the fossil record suggests that the modern turtle ear evolved during the Early to Middle Jurassic in stem turtles broadly adapted to freshwater and terrestrial settings. This, combined with our finding that evolutionary transitions between habitats caused only weak evolutionary changes in middle ear structure, suggests that tympanic hearing in turtles evolved as a compromise between subaerial and underwater hearing.</p>
Copper And Wood Ear Disc (71a983)
**Copper-and-wood ear disc** Location: Town Creek site (31Mg2-3), Montgomery County, North Carolina. Period: Mississippian (AD 1150-1400). Material: copper and wood. Dimensions: length, 53.7 mm; width, 47.6 mm; thickness, 5.5 mm. Notes: Catalog no. 71a983, North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Chris LaMack. Source: Objaverse 1.0 / Sketchfab
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Allen Brain Atlas
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International Brain Laboratory public data
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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.