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32 results for “visual features”
Sharpening of Hierarchical Visual Feature Representations of Blurred Images
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Acoustic features as a tool to visualize and explore marine soundscapes: Applications illustrated using marine mammal Passive Acoustic Monitoring datasets
<p>Passive Acoustic Monitoring (PAM) is emerging as a solution for monitoring species and environmental change over large spatial and temporal scales. However, drawing rigorous conclusions based on acoustic recordings is challenging, as there is no consensus over which approaches, and indices are best suited for characterizing marine and terrestrial acoustic environments.</p> <p>Here, we describe the application of multiple machine-learning techniques to the analysis of a large PAM dataset. We combine pre-trained acoustic classification models (VGGish, NOAA & Google Humpback Whale Detector), dimensionality reduction (UMAP), and balanced random forest algorithms to demonstrate how machine-learned acoustic features capture different aspects of the marine environment.</p> <p>The UMAP dimensions derived from VGGish acoustic features exhibited good performance in separating marine mammal vocalizations according to species and locations. RF models trained on the acoustic features performed well for labelled sounds in the 8 kHz range, however, low and high-frequency sounds could not be classified using this approach.</p> <p>The workflow presented here shows how acoustic feature extraction, visualization, and analysis allow for establishing a link between ecologically relevant information and PAM recordings at multiple scales.</p> <p>The datasets and scripts provided in this repository allow replicating the results presented in the publication. </p>
BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 4: Visual stimuli
<p>In order to test the developed features, the SSVEP datasets recorded in (Nakanishi et al. 2014) is used. Flickering boxes had been presented on 24-inch LCD monitor with a refresh rate of 75Hz. 32 visual stimuli had been generated with 8 different frequencies (8 Hz, 9 Hz, …, 15 Hz) and 4 different phases (0<sup>o </sup>, 90<sup>o</sup> , 180<sup>o</sup> , 270<sup>o</sup> ) as shown in Figure 4. Thirteen healthy adults had participated in the experiments. EEG data had been recorded by 16 electrodes (FPz, F3, F4, Fz, C<sub>z</sub>, P1, P2, P<sub>z</sub>, PO3, PO4, PO7, PO8, PO<sub>z</sub>, O1, O2 and Oz). The sampling rate had been 512 Hz. The datasets are grouped into 4 groups. The 0-degree stimuli formed the 1st group, the 90- degree stimuli the 2<sup>nd</sup> group, the 180-degree stimuli the 3<sup>rd</sup> group and the 270-degree stimuli the 4<sup>th</sup> group. Thus, it is made possible to test the developed features in more datasets. </p>
Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction
<p>Representative Testing/Validation WSIs used in the manuscript "Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction"</p>
Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction
<p>Training image dataset used in the manuscript "Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction"</p>
Acoustic features as a tool to visualize and explore marine soundscapes: Applications illustrated using marine mammal Passive Acoustic Monitoring datasets
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Data, stimuli, and analyses for "High-level aftereffects reveal the role of statistical features in visual shape encoding"
<h2><strong>Data and code share.</strong></h2><p>This record contains data and code (written in MATLAB) to reproduce the results shown in:</p><p>Morgenstern, Y. , Storrs, K., R., Schmidt, F., Hartmann, F., Tiedemann, H., Tiedemann, H, ., Wagemans, J., & Fleming, R. (<i>in press</i>) . High-level aftereffects reveal the role of statistical features in visual shape coding. Current Biology</p><p>Below is a summary of shared scripts that load data, run the analysis (including options for fitting model parameters or loading pre-computed fitted parameters), and plotting the results.</p><h3><strong>Figure 1</strong></h3><ul><li><i>Fig1C_shapespace.m</i>: draw shape space (as in Figure 1C)</li><li><i>Fig1EFG_plotpsychometricdata.m</i>: fit psychometric model to pooled data and plot (as in Figure 1EFG)</li><li><i>getExptShapesHbias.m</i>: saves a data structure (which we call 'package') with adaptor, test, and human biases from experiment 1. (Used to fit models; e.g., see <i>fitGabPyr2Hbais.m</i> or <i>fitTAEGANfit2Hbais.m</i>)</li></ul><h3><strong>Figure 2 and 3A</strong></h3><ul><li><i>fig3A_modeval_expt1.m</i>: generate figure that evaluates models in Figure 3A on how well they predict aftereffects in Experiment 1. (script located in the 'Figures 2 and 3A/models' directory).</li></ul><p>Code to fit the models, and figures that show examples of model predictions are in the model directories, and summarized below:</p><h4>Model: <strong>GabPyrAE</strong> </h4><ul><li><i><strong>note: </strong></i>To run GabPyr, you will likely need to recompile the .mex files in 'matlabPyrTools/mex'. Then move the recompiled files into the 'matlabPyrToos' directory</li><li><i>fig2B_GabPyrAEVisFigs.m</i>: produce GabPyrAE model images for example adaptor and test image ( as in Figure 2B )</li><li><i>figS2BC_GabPyrAEExp</i>.m: get GabPyrAE model responses to simulated tilt aftereffect experiment using anisotropic noise, and plot model responses as in Figures S2BC.</li><li><i>getGabPyrAEMod.m: </i>given an adaptor and test image, this function produces the unfit GabPyrAE prediction</li><li><i>fitGabPyr2Hbias</i>.m: fit GabPyrAE model to best predict human baises in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitGabPyrNormMod2Stims</i>.m</li><li><i>evalGabPyrAE_fitmod_aic.m</i>: evaluate GabPyrAE fitted model on how well it predicts human biases from experiment 1.</li><li><i>evalGabPyrAE_unfitmod_aic.m</i>: evaluate GabPyrAE fitted model on how well it predicts human biases from experiment 1</li></ul><h4>Model: <strong>TAE</strong></h4><ul><li><i>fig2CD_TAEmod.m</i>: produce TAE model images for example adaptor and test image (as in Figure 2CD)</li><li><i>getTAEModonShape.m: </i>given an adaptor and test image, this function produces TAE Original prediction. Input to function is adaptor and test shapes, and TAE model parameters alpha and sigma.</li><li><i>getTAEGAN_spwt_onShape.m: </i>given an adaptor and test image, this function produces TAEGAN prediction. Input to function is adaptor and test shapes, and TAEGAN model parameters which include a constant term, alpha and sigma, as well as Gaussian pooling parameter that determines how much TAE to incorporate on the test or mean shapes from neighbouring adaptor line segments.</li><li><i>getTAE_spwt_onShape_nn.m: </i>given an adaptor and test image, this function produces TAE nearest neighbour prediction. Input to function is adaptor and test shapes, and TAE nearest neighbour model parameters which include a constant term, alpha and sigma, as well as Gaussian pooling parameter that determines how much TAE to incorporate on the test or mean shapes from neighbouring adaptor line segments.</li><li><i>fitTAEGAN2Hbias</i>.m: fit TAEGAN model to best predict human biases in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitTAEGANMod2Stims</i>.m</li><li><i>fitTAENN2Hbias</i>.m: fit TAE nearest neighbour model to best predict human biases in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitTAENNMod2Stims</i>.m.</li><li><i>evalTAEGAN_fitmod_aic.m</i>: evaluate TAEGAN fitted model on how well it predicts human biases from experiment 1.</li><li><i>evalTAENN_fitmod_aic.m</i>: evaluate TAE nearest neighbour fitted model on how well it predicts human biases from experiment 1.</li><li><i>evalTAE_unfitmod_aic.m</i>: evaluate TAE Original model on how well it predicts human biases from experiment 1</li></ul><h4>Model: <strong>PSAE</strong></h4><ul><li><i>fig2EF_PSAEmod.m</i>: produce PSAE model images for example adaptor and test image</li><li><i>getPos_spwt_ShiftononShape_io.m: </i>given an adaptor and test image, this function produces PSAEGAN prediction. Input to function is adaptor and test shapes, and PSAEGAN model parameters which include a constant term, alpha and sigma, as well as Gaussian pooling parameter that determines how much PSAE to incorporate on the test or mean shapes from neighbouring adaptor line segments.</li><li><i>getPos_spwt_ShiftonShape_io_nn.m: </i>given an adaptor and test image, this function produces PSAE nearest neighbour prediction. Input to function is adaptor and test shapes, and PSAE nearest neighbour model parameters which include a constant term, alpha and sigma, as well as Gaussian pooling parameter that determines how much PSAE to incorporate on the test or mean shapes from neighbouring adaptor line segments.</li><li><i>fitPSAEGAN2Hbias</i>.m: fit PSAEGAN model to best predict human biases in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitPSAEGANMod2Stims</i>.m</li><li><i>fitPSAENN2Hbias</i>.m: fit PSAE nearest neighbour model to best predict human biases in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitPSAENNMod2Stims</i>.m.</li><li><i>evalPSAEGAN_fitmod_aic.m</i>: evaluate PSAEGAN fitted model on how well it predicts human biases from experiment 1.</li><li><i>evalPSAENN_fitmod_aic.m</i>: evaluate PSAE nearest neighbour fitted model on how well it predicts human biases from experiment 1.</li></ul><h4>Model: <strong>ShapeComp </strong>and <strong>No Adaptation</strong></h4><ul><li><i>eval_ShapeComp _aic.m</i>: evaluate ShapeComp 1 parameter fitted model on how well it predicts human biases from experiment 1.</li><li><i>eval_NoAdaptation _aic.m</i>: evaluate model that predicts no adaptation on how well it predicts human biases from experiment 1.</li></ul><h3><strong>Figure 3BC</strong></h3><ul><li><i>fig3BC_Experiment2.m</i>: load, analyze, and plot experiment 2 data (as in Figure 3B and C).</li><li><i>figS4_Expt2_stimuli.m</i>: show adaptors (in black) and test shapes for ShapeComp (purple), PSAE fit GAN (green), and no adaptation model (white) (as in Figure S4)</li></ul><p> </p>
Audio, visual, and verbal features from AVI video interviews in Koutsoumpis et al., 2024 (opva)
<p>The folder contains the audio, visual, and verbal features extracted from video data collected by Koutsoumpis et al., 2024 (where details on the data are provided). Details on data extraction can be found in Ghassemi et al., 2023. </p> <p>Files starting with "t1" refer to extracted audio, visual, and verbal features from Time 1. Files starting with "t2" refer to extracted audio, visual, and verbal features from Time 2, which took place 7 to 24 months after Time 1. In Time 2, a sub-sample of participants from Time 1 took the exact same study a second time.</p> <p> </p> <p>Ghassemi, S., Zhang, T., Van Breda, W.R.J., Holtrop, D.J., Oostrom, J.K., Koutsoumpis, A., & De Vries, R.E. (2023). Unsupervised multimodal learning for dependency-free personality recognition. IEEE Transactions on Affective Computing, 15(3), 1053-1066. https://doi.org/10.1109/TAFFC.2023.3318367</p> <p>Koutsoumpis, A., Ghassemi, S., Oostrom, J.K., Holtrop, D.J., Van Breda, W., Zhang, T. & De Vries, R.E. (2024). Beyond traditional job interviews: Psychometric analysis of asynchronous job video interviews for personality and interview performance evaluation using machine learning. Computers in Human Behavior, 154(108128). https://doi.org/10.1016/j.chb.2023.108128</p>
Visual features in the perception of liquids
<p>Dataset relative to the following publication:</p> <p>van Assen, J.J.R., Pascal Barla & Fleming, R. W. (2018). Visual features in the perception of liquids. <em>Current Biology.</em></p> <p>One zip file contains the datasets of the various experiments and code to run the analysis.</p> <p>The second zip file contains the liquid stimuli used in this study, note: this is a large file (~12Gb).</p>
Visualization of Idealized Atmospheric-River-like Features
<p>In an idealized simulation of a shallow-water fluid, all the poleward transport of a water vapor-like tracer transport is produced by AR-like filamentary structures. This dry simulation is initialized with a barotropically unstable midlatitude jet stream and a passive tracer in the tropics. Due to the idealized nature of the simulation, we can unequivocally identify the formation of a Rossby wave in the jet stream. In the tracer field, this wave leads to turbulent stirring and generates long, narrow filamentary structures that have a similar aspect ratio as ARs. Based on our experiment setup, these structures should be responsible for all the meridional tracer transport in the extratropics. </p>
Dataset: Scatter of mechanical properties of 2D biaxial and triaxial braided reinforced composites and their correlation with visual features
<p>The data represents tensile tests of 2D braided biaxial and triaxial composite laminates. Braids were produced on a Herzog radial braiding machine RF 1-176-100 with carbon fiber yarns (Toho Tenax F13 HTS40 12K 800 tex). Test laminates are infused with a VARI process with the 2-component epoxy resin system Hexion RIM235/RIMH237. The infusion is performed at vacuum pressure leves of 20-30 mbar and the resin is cured at room temperature for 24 hours at a pressure level of 250 mbar. A subsequent post-cure step maximizes matrix properties at 70°C for 10 hours.<br> The braid setups have been chosen to achieve four different braids with a fiber angle of 45° on braid mandrels with a diameter of 120 mm, two biaxial ones and two triaxial ones. By using different sizes of braiding ring diameters and yarn forces, homogeneous and inhomogeneous fiber architectures are generated.<br> Braiding setups 1 (biaxial) and 2 (triaxial) with 130 mm braiding ring diameter and 350 g yarn force result in braids with homogeneous fiber architecture. 3 (biaxial, 400 mm braiding ring, 600 g yarn force) and 4 (triaxial, 250 mm braiding ring, 600 g yarn force) in braids with inhomogeneous scattering of visual characteristics. Single layers are braided on mandrels with a length of 1500 mm and laminates with 6 layers of biaxial and 4 layers of triaxial braids are subsequently infused.<br> Tensile tests are performed on a Schenck-Trebel RM250 universal testing machine according to AITM 1-0007. For each specimen, the individual fiber orientation in each layer is measured prior to infusion by image analysis performed on scans of the braided layers. The results show a strong dependence of the individual fiber orientation of single test specimens on their mechanical properties in case of the presented tensile tests</p>
Processing load, and not stimulus evidence, determines the duration of unconscious visual feature integration
<p>Data and MATLAB analysis script for the paper "Processing load, and not stimulus evidence, determines the duration of unconscious visual feature integration"</p>
Supplementary information for the manuscript 'GFViz: A tutorial on creating interactive visualization of genomic features using R Tidyverse and plotly'
<p>This is the supplementary information of the manuscript 'GFViz: A tutorial on creating interactive visualization of genomic features using R Tidyverse and plotly' published as a part of the thesis 'Annotating and making use of the <em>Avena sativa</em> cv. Sang reference genome' by Nikos Tsardakas Renhuldt.</p>
Probing the Role of Feature Dimension Maps in Visual Cognition: Impact of Salience Level (Eye-tracking Follow-up Study)
ClinicalTrials.gov study NCT06852534. IPD Sharing: YES. Countries: 1. Publications: 14.
Distinct attentional characteristics of neurons with visual feature coding in the primate brain
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Raw data from: Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila, part 2
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Data from: Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila
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Raw data from: Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila, part 3
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Raw data from: Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila, part 1
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Dual-feature selectivity enables bidirectional coding in visual cortical neurons
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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.