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126 results for “Computational Detection”
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 3.Proposed Computer Assisted Diagnosis
<p>The figure below presents our proposed Computer Assisted Diagnosis. Our CAD includes 3 steps: Preprocessing, Segmentation and Classification. For the step of preprocessing, we used the NLMS (Non Local Means) to improve the quality of image. For the step of segmentation: we have a learning phase to extract the different shapes and to determine the average shape. Our proposed automatic method is based on the deformable model. For the step of classification, we present a new supervised method to distinguish between Normal, MCI and AD. The figure below presents our proposed system.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 4. Training
<p>Each shape will be modeled by a vector X, built by concatenating the coordinates of the characteristic points placed on its outline: X=(X1, X2,…...Xn) (1) The training set can be modeled by a set of vectors: {Xi} Where i = 1. . N {N number of sample images} and {Si} surface, {Vi} standard deviation of the Area. The principle of this step is be illustrated by the figure below. </p>
High spatiotemporal resolution free surface detection using cost-effective video equipment and computer vision techniques in nearly stationary flow along a transparent wall in the laboratory
<p>The identification of the air-water interface in free surface flows traditionally involves intrusive techniques or costly equipment. Non-intrusive alternatives, such as computer vision, are emerging as highly effective substitutes or supplements for more invasive techniques in laboratory measurements, thanks to their straightforward implementation and cost efficiency. This research specifically delves in the conjunction of various naive techniques, exploring their collective precision in detecting the air-water interface along transparent walls in laboratory. A detection technique based on the double gradient of the image is applied and thoroughly examined. The study progresses through multiple refinement stages, culminating in a method that is both cost effective and easy to implement. This methodology allows for large-scale, high resolution measurements (200 mm × 1800 frames per video at a 0.25 mm, 50 Hz resolution), offering both spatial and temporal measurements by adeptly detecting the free surface along transparent walls.</p>
EOL computer vision pipelines: Object Detection for Image Cropping: Aves
<p>Produced by an detection model pretrained on MS COCO 2017. Automatically crops images of birds (Aves) to square dimensions centered around animal(s). </p> <p>388,166 rows </p>
EOL computer vision pipelines: Object Detection for Image Cropping: Multi-taxon
<p>Produced by EOL Multitaxon Object Detection Model. Automatically crops images of snakes & lizards (Squamata), beetles (Coleoptera), frogs (Anura), and carnivores (Carnivora) to square dimensions centered around animal(s). Model available in the <a href="https://www.kaggle.com/models/eolorg/multitaxa-crops-thumbnails" target="_blank" rel="noopener">EOL Model Zoo on Kaggle</a>.</p> <ul> <li>Anura = 42646 rows</li> <li>Coleoptera = 115276 rows</li> <li>Squamata = 132680 rows</li> <li>Carnivora = 31132 rows</li> </ul>
EOL computer vision pipelines: Object Detection for Image Cropping: Chiroptera
<p>Produced by EOL Chiroptera Object Detection Model. Automatically crops images of bats (Chiroptera) to square dimensions centered around animal(s). Model available in the <a href="https://www.kaggle.com/models/eolorg/chiroptera-crops-thumbnails" target="_blank" rel="noopener">EOL Model Zoo on Kaggle</a>.</p> <p> </p> <p>17,401 rows</p>
EOL computer vision pipelines: Object Detection for Image Cropping: Lepidoptera
<p>Produced by EOL Lepidoptera Object Detection Model. Automatically crops images of butterflies and moths (Lepidoptera) to square dimensions centered around animal(s). Model available in the <a href="https://www.kaggle.com/models/eolorg/lepidoptera-crops-thumbnails" target="_blank" rel="noopener">EOL Model Zoo on Kaggle</a>.</p> <p> </p> <p>608,163 rows</p> <p> </p>
REMODEL. WP4. Vision-Based Perception. T4-4. Functional component detection. Data related to a paper presented at 27th International Conference on Automation and Computing (ICAC) (2022)
<p>Dataset with evaluation parameters of the paper "Real-Time Instance Segmentation of Pedestrians using Transfer Learning", DOI: <a href="https://doi.org/10.1109/ICAC55051.2022.9911121">10.1109/ICAC55051.2022.9911121</a></p>
Data from: Testing background matching and disruptive colouration in a sexually dichromatic grasshopper: a computer detection experiment.
<p>Cryptic colouration is an adaptative mechanism against predators. Colour patterns can become cryptic through background matching and disruptive colouration, which breaks up the outlines of an animal because the pattern does not coincide with the shape and outline of the animal’s body. Background matching could be advantageous in chromatically homogeneous microhabitats, whereas disruptive colouration can be favoured in visually heterogeneous microhabitats. Grasshoppers of the genus <em>Sphenarium</em> (Orthoptera: Pyrogomorphidae) inhabit very heterogeneous environments and exhibit both strategies. Adults show substantial continuous variation in colouration and longitudinal and transverse bands on the thorax and abdomen. However, males often exhibit considerably more variation in the number of longitudinal and transverse bands than females, which tend to have more uniform colouring (flatter patterns). In this study, we analysed the cryptic properties of the colour patterns of males and females of <em>Sphenarium </em><em>zapotecum</em><em> </em>Sanabria-Urbán, H. Song & Cueva del Castillo and tested the effectiveness of background matching and disruptive colouration using humans as ‘predators’ in a computer detection experiment. We found that the females and males are dichromatic and seem to follow different cryptic strategies in their colouration: males are more disruptive to the background than females, whereas females have a higher level of background matching. In addition, in visually heterogeneous areas, predators spent most time searching for striped male morphs with lower background matching and higher disruptive properties, as well as for female morphs with high background matching, potentially increasing prey survival. As background matching is associated with females and disruptive colouration with males, our results could help explain the evolution of sexual dichromatism in this and other species of grasshoppers of the genus <em>Sphenarium.</em></p> <p> </p>
Dataset for publication 'Automated computed tomography based parasitoid detection in mason bee rearings'
<p>Dataset corresponding to the publication <strong>Automated computed tomography based parasitoid detection in mason bee rearings </strong>published at PLOS One. This repository contains the training, validation and test data.</p>
Dataset for the article "Efficient Computation of Magnetic Polarizability Tensor Spectral Signatures for Object Characterisation in Metal Detection"
<p>Datasets to accompany the article "Efficient Computation of Magnetic Polarizability Tensor Spectral Signatures for Object Characterisation in Metal Detection". Written by J. Elgy and P. D. Ledger. </p> <p>The datasets include data files, meshes, and code for recreating the results from the paper. This requires the open source MPT-Calculator software available at <a href="http://github.com/MPT-Calculator/MPT-Calculator">https://github.com/MPT-Calculator/MPT-Calculator</a> (v.1.5.0).</p> <p>J. Elgy and P. D. Ledger gratefully acknowledge the financial support received from EPSRC in the form of grant EP/V009028/1.</p>
Simulation dataset for "Computational pan-genome mapping and pairwise SNP-distance improve detection of Mycobacterium tuberculosis transmission clusters"
<p>Simulated Illumina reads for SNP distance method evaluation and comparison used in the article "Computational pan-genome mapping and pairwise SNP-distance improve detection of Mycobacterium tuberculosis transmission clusters".</p> <p>Details for simulation can be found at https://gitlab.com/rki_bioinformatics/panpasco/tree/master/simulation_dataset.</p>
CLDF dataset underlying the study "First steps towards the detection of contact layers in Bangime: a multi-disciplinary, computer-assisted approach" from 2022
<p>Cite the source of the dataset as:</p> <blockquote> <p>Hantgan, Abbie and Babiker, Hiba and List, Johann-Mattis (2022): First steps towards the detection of contact layers in Bangime: a multi-disciplinary, computer-assisted approach [version 2; peer review: 2 approved]. Open Research Europe 2022, 2:10.</p> </blockquote>
Data from: Scoring thermal limits in small insects using open-source, computer assisted motion detection
<p>Scoring large amounts of thermal tolerance traits live or with recorded video can be time consuming and susceptible to investigator bias, and as with many physiological measurements, there can be trade-offs between accuracy and throughput. Recent studies show that particle tracking is a viable alternative to manually scoring videos, although it may not detect subtle movements, and many of the software options are proprietary and costly. In this study, we present a novel strategy for automated scoring of thermal tolerance videos by inferring motor activity with motion detection using an open-source Python command line application called DIME (Detector of Insect Motion Endpoint). We apply our strategy to both dynamic and static thermal tolerance assays, and our results indicate that DIME can accurately measure thermal acclimation responses, generally agrees with visual estimates of thermal limits, and can significantly increase the throughput over manual methods.</p>
Active Close Contact Investigation of Tuberculosis Through Computer-aided Detection and Stool Xpert MTB/RIF Among People Living in Ethiopia
ClinicalTrials.gov study NCT05818059. IPD Sharing: NO. Countries: 1. Publications: 1.
Evaluation of Computer-Aided Lung Nodule Detection Software in Thoracic CT for Riverain Technologies LLC
ClinicalTrials.gov study NCT02440139. IPD Sharing: NO. Countries: 1. Publications: 6.
Testing of Computer Aided Detection Software for Riverain Medical Group
ClinicalTrials.gov study NCT00906789. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Bridging the analog divide: A comparison of printed X-ray films and digital images when using computer-aided detection software for tuberculosis screening
Open the record for dataset details and reuse information.
Data from: Scoring thermal limits in small insects using open-source, computer assisted motion detection
Open the record for dataset details and reuse information.
Targeted indication of imaging for detection of vesicoureteric reflux after pediatric febrile urinary tract infections based on a multiparametric computational tool
<p>This is the original data used for our study on differentiated indication of a voiding cystourethrography after pediatric febrile urinary tract infections. </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.