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1,025 results for “Vision”
Dataset for 'Intraocular scatter compensation with spatial light amplitude modulation for improved vision in simulated cataractous eyes'
<p>Dataset for the manuscript entitled: Intraocular scatter compensation with spatial light amplitude modulation for improved vision in simulated cataractous eyes</p> <p>includes:</p> <p>1) PSF from numerical simulations</p> <p>2) CSF measured in subjects</p> <p>3) Michelson contrast from numerical simulations</p>
Natural Image Statistics for Mouse Vision
<p>This repository contains 232 "mouse-view" UV/Green natural scene images for the mouse vision, acquired by a custom multi-spectral camera. For details, please see the following paper:</p> <p> Natural Image Statistics for the Mouse Vision<br> Luca Abballe and Hiroki Asari<br> PLOS One (accepted)</p> <p>The preprint is also available from bioRxiv 2021.04.08.438953.</p>
Data for: A hypothesis for robust polarization vision: An example from the Australian Imperial Blue butterfly, Jalmenus evagoras
<p class="MsoNormal"><span>The Australian lycaenid butterfly, <em>Jalmenus evagoras</em>, has iridescent wings that are sexually dimorphic in both spectral reflection and degree of polarization, </span><span>suggesting</span><span> </span><span>that these wing properties are likely to be important in mate recognition. We first describe the results of a field experiment </span><span>showing<span> that free-flying individuals of <em>J. evagoras </em>discriminate between visual stimuli that vary in polarization content in blue wavelengths but not in others. We then present detailed reflectance spe</span>ctrophotometry</span><span> </span><span>measurements <span>of the polarization content of male and female wings, showing that female wings exhibit blue-shifted reflectance,</span> with</span><span> </span><span>a lower degree of polarization relative to male wings. </span><span>Finally, we describe a novel method for measuring alignment of ommatidial arrays:<span> </span>By measuring variation of depolarized eyeshine intensity from </span><span>patches of</span><span> ommatidia as a function of eye rotation, we show that </span><span>a) </span><span>individual </span><span>rhabdoms</span><span> contain mutually perpendicular microvilli</span><span>; b) many rhabdoms in the array</span><span> are rotated with respect to one another by as much as 45º</span><span>; c) the rotated ommatidia are useful for robust polarization detection. </span><span>By mapping the distribution of the ommatidial </span><span>rotations</span><span> in eye </span><span>patches</span><span> of <em>J. evagoras</em>, we show that males and females exhibit differences in the extent to which </span><span>ommatidia</span><span> are aligned</span><span>. Both</span><span> the </span><span>number</span><span> of </span><span>rotated </span><span>ommatidia suitable for </span><span>robust </span><span>polarization-detection</span><span>, and the number of aligned ommatidia suitable for</span><span> edge-detection</span><span>,</span><span> var</span><span>y</span><span> with respect to both sex and eye</span><span>-</span><span>patch elevation. </span><span>Thus, <em>J. evagoras </em>exhibits finely-tuned ommatidial arrays suitable for perception of polarized signals, likely to match sex-specific life history differences in the utility of polarized signals.</span></p>
Microclimate simulation output: "Between vision and action: the predicted effects of co-designed green infrastructure solutions on environmental burdens"
<p>The following microclimate simulation dataset supports the paper "Between vision and action: the predicted effects of co-designed green infrastructure solutions on environmental burdens" by Mathias Schaefer, published in Urban Ecosystems (2022).</p> <p>"T0Simulation_11082020_output" contains data about the status quo simulation of the area of interest (500 m x 500 m x 60 m), whereas "T1Simulation_11082020_output" shows the results of the Green Infrastructure scenario described in the research article above. Please ensure enough memory space on your device, as both files have a size of approximately 25 GB (unzipped).</p> <p>The output files can be visualized with the ENVI-met Leonardo extension. The ENVI-met LITE-version is freely available and can be downloaded at the <a href="https://envi-met.info/doku.php?id=files:download">ENVI-met homepage</a>. Alternatively, the included .NETCDF files can be imported as a multidimensional raster dataset in ArcGIS Pro.</p> <p>Files in the folder "atmosphere" represent meteorological parameters such as potential air temperature [°C], relative humidity [%], or wind speed [m/s]. Air pollution calculations like particulate matter concentrations [µg/m³] can be found in the folder "pollutants". The folder "buildings" contains building data for 3D visualizations of surface temperatures [°C].</p>
A Priority Map for Vision-Language Navigation - Datasets
<p>This archive contains full versions of the datasets and additional data presented in the following paper:</p> <p>A Priority Map for Vision-and-Language Navigation with Trajectory Plans and Feature-Location Cues</p> <p>A priority map module (PM-VLN) boosts the performance of transformer-based architectures in navigation tasks by combining temporal sequence alignment and feature-level localisation in cross-modal inputs. The module is pretrained on trajectory estimation and a multi-objective task that pairs location estimation with cross-modal sentence prediction. Two datasets are introduced for the auxiliary tasks:</p> <p> - TR-NY-PIT-central - a set of path traces for routes in two urban locations.</p> <p> - MC-10 - a set of samples with multimodal inputs representing landmarks in 10 US cities.</p> <p>Full details and links for this research are available at the following link:</p> <p>https://jasonarmitage-res.github.io/projects/priority_map/</p> <p>Additional data comprising path traces for routes in Manhattan and language tokens for the Touchdown task are provided for training and evaluating the PM-VLN and framework on the Touchdown benchmark. Please refer to the following link for details on the Touchdown dataset and StreetLearn environment:</p> <p>https://sites.google.com/view/streetlearn/touchdown</p>
Brain Tumor MR Image Data Set For Machine Vision Approach for Brain Tumor Classification using Multi Features Dataset
<p>The uploaded dataset contains the brain tumor MRI dataset. The dataset has been collected form the Bahawal Victoria Hospital, Bahawalpur, Pakistan. This dataset is an authorized MRI brain tumor dataset. Is has been authorized from the expert Radiologists of the Bahawal Victoria Hospital <a href="https://www.qamc.edu.pk/administration/2">BVH</a>. The dataset consists of three brain tumor types, namely adenomas, meningioma and glioma. it is only for academic, educational and experimental purpose. no other usage will be owned or any liability will be accepted by the authors.</p>
The HAInich: A multidisciplinary vision data-set for a better understanding of the forest ecosystem
<p>We present a multidisciplinary forest ecosystem 3D perception dataset. The dataset was collected in the Hainich-Dün region in central Germany, which includes two dedicated areas, which are part of the Biodiversity Exploratories - a long term research platform for comparative and experimental biodiversity and ecosystem research. The dataset combines several disciplines, including computer science and robotics, biology, bio-geochemistry, and forestry science. We present results for common 3D perception tasks, including classification, depth estimation, localization, and path planning. We combine the full suite of modern perception sensors, including high-resolution fisheye cameras, 3D dense LiDAR, differential GPS, and an inertial measurement unit, with ecological metadata of the area, including stand age, diameter, exact 3D position, and species. The dataset consists of three hand held measurement series taken from sensors mounted on a UAV during each of three seasons: winter, spring, and early summer. This enables new research opportunities and paves the way for testing forest environment 3D perception tasks and mission set automation. We do not focus on even more accurate and better forest data collection, our focus is automated forest inventory for robots.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 7. Effect of different type of movement on the image in the vision of artificial creature.
<p>Figure 7a, Figure 7b, Figure 7c and Figure 7d, shows effect of different type of movement on the image in the vision of artificial creature if food be on vision boundaries. As mentioned each part of the image equal 7.5 degree.<br> Therefore 15 degree left or right rotation locomotion equivalent two parts shift toward left or right.<br> For motion to forward direction, size of the image has been reduplicated so that each part has been become to the two similar parts. Then half of new image in right side and left side has been deleted in order to create new close image in vision. Accordingly, if food be on vision boundaries, the number of black parts of the image for the food object in ultimate location is 2, by one movement to forward direction the number of these parts become to 4, by one movement to forward direction the number of these parts become to 8 and so on. After four movements to forward all part of the image is black and the creature is succeed find the food object.</p>
Figure 9. Trajectory Algorithm Simulation-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>We have presented the system for a fully autonomous navigation of an UAV based on Omni<br> directional vision system and image processing. we explain vision system configuration ,image<br> processing and feature extraction methods and finaly suggest an algorithm based on potential field<br> for navigation of an UAV.</p>
Figure 8. Potential at every point; it is highest in the obstacles and lowest at the goal-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>The numerical potential field path planner is guaranteed to produce a<br> path even if the start or goal is placed in an obstacle. If there is no possible way to get from the start<br> to the goal without passing through an obstacle then the path planner will generate a path through<br> the obstacle, although if there is any alternative then the path will do that instead. For this reason, it<br> is important to make sure that there is some possible path, although there are ways around this<br> restriction such as returning an error if the potential at the start point is too high. The path is found<br> by moving to the neighboring square with the lowest potential, starting at any point in the space and<br> stopping when the goal is reached.</p>
Figure 7. Obstacle force (repulsive potential) and goal force obstacle force-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>Since the motion trajectory of UAV is divided into several median points that the UAV<br> should reach them one by one in a sequence the output obtained after the execution of AI will be a<br> set of position and velocity vectors. So the task of the trajectory will be to guide the UAV through<br> the obstacles to reach the destination. The routine used for this purpose is the potential field method<br> (also an alternative new method is in progress which models the UAV motion through opponents<br> same as the owing of a bulk of water through obstacles) [5]. In this method, different electrical<br> charges are assigned to UAV, obstacles, and the destination. Then by calculating the potential field<br> of this system of charges a path will be suggested for the UAV.</p>
Figure 6. Goal force-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>Since the motion trajectory of UAV is divided into several median points that the UAV<br> should reach them one by one in a sequence the output obtained after the execution of AI will be a<br> set of position and velocity vectors. So the task of the trajectory will be to guide the UAV through<br> the obstacles to reach the destination. The routine used for this purpose is the potential field method<br> (also an alternative new method is in progress which models the UAV motion through opponents<br> same as the owing of a bulk of water through obstacles) [5]. In this method, different electrical<br> charges are assigned to UAV, obstacles, and the destination. Then by calculating the potential field<br> of this system of charges a path will be suggested for the UAV. At a higher level, predictions can be<br> used to anticipate the position of the obstacles and make better decisions in order to reach the<br> desired vector. In our path- planning algorithm, an articial potential field is set up in the space; that<br> is, each point in the space is assigned a scalar value. The value at the goal point is set to be 0 and the<br> value of the potential at all other points is positive.</p>
Figure 5. Images binarized by SVM.-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>In instance, the image in Fig. 4 was binarized manually (however, the person in charge of<br> color adjustment is professional) and by using SVM resulting in Fig.5</p>
Figure 4. Images binarized by hand.-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>In instance, the image in Fig. 4 was binarized manually (however, the person in charge of<br> color adjustment is professional) and by using SVM resulting in Fig.5</p>
Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System-Figure 3. The principle of SVM
<p>Manual segmentation training was hard and very time consuming and rely to operator's<br> accuracy, so we developed a color calibration algorithm using SVM(Support Vector Machine). In<br> this subsection, we present a color recognition algorithm using the support vector machine<br> (SVM).SVM is one of the classification algorithms which it has high generality since it can<br> calculate a super plane that maximizes the margin of classes, Fig.3.[8] In our algorithm, the SVM is<br> trained by the H'SY values of the classes and the mean of the obtained image H'SY values. After<br> training, the obtained image is binarized by setting the maximum and minimum value in the<br> distribution of each class as a threshold.</p>
Figure 2. Catadioptric projection modelled by the unit sphere-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>Since the beginning of UAV, the map building was one of the most addressed problems by<br> researchers. Several researchers used Omni directional vision for robot navigation and map<br> building. Because of the wide field of view in Omni directional sensors, the robot does not need to<br> look around using moving parts (cameras or mirrors) or turning the moving parts. The global view<br> offered by Omni directional vision is especially suitable for highly dynamic environments. The<br> Omni directional vision system consists of a hyperbolic mirror, a USB color digital camera<br> (Logitech C905) and a regulation device.</p>
Figure 1. The use of visual servo control for helicopter stabilization-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>Visual servoing is an approach to control motion of a helicopter using information feedback<br> from a camera mounted on it. For their tremendous potential applications in various areas including<br> environmental monitoring and anti-terrorism, unmanned small helicopters are being extensively<br> studied in robotics and control in recent years. However, the research advance in dynamic control of<br> small helicopters is limited due to highly coupled non- linear dynamics and the existence of various<br> uncertain- ties. Many people studied controller design based on a Publisher Item Identifier.<br> linearized or simplified model, but the controllers developed under linearized models cannot<br> guarantee dynamic stability rigorously. Another effort is application of modern non-linear control<br> theory to helicopter control because small helicopter are good test beds for sophisticated control<br> techniques for their small size and highly coupled dynamics [4].</p>
Supplementary Materials for Learning Manufacturing Computer Vision Systems Using Tiny YOLOv4
<h1>About This Dataset</h1> <p>This repository contains the supplementary materials presented in the publication “Learning Manufacturing Computer Vision Systems Using Tiny YOLO v4” by Medina, A., Bradley, R., Xu, W., Ponce, P., Anthony, B., and Molina, A. that can be found with the following DOI <a href="https://www.frontiersin.org/articles/10.3389/frobt.2024.1331249/">10.3389/frobt.2024.1331249</a></p> <p>There are three files in this repository:</p> <ol> <li>dataset.zip</li> <li>YOLOv4_object_detection.ipynb</li> <li>deploy.py</li> </ol> <h1>dataset.zip</h1> <p>This Dataset is for an example used for education purposes. It is a small dataset that is adapted from the following Kaggle repository, authored by Ruthger Righart <a href="https://www.kaggle.com/datasets/rrighart/jarlids/data">https://www.kaggle.com/datasets/rrighart/jarlids/data</a>. One of the activities proposed is to teach students how to find, download and review a free dataset, so this is the example given.</p> <p>Another activity is to teach how to label images to create a custom dataset. The images (with extension .JPG) from the original repository are used. The labels (with extension .txt) were created by the authors of the Learning Manufacturing Computer Vision Systems Using Tiny YOLOv4 paper. The authors used the free tool labelImg, from GitHub repository (<a href="https://github.com/HumanSignal/labelImg">https://github.com/HumanSignal/labelImg</a>), to label the images with object bounding boxes and corresponding labels in the YOLO format.</p> <p>The dataset contains 238 images and corresponding labels, with files named “p<num>.JPG” and “p<num>.txt”. The text labels are formatted in the YOLO format with each row in the .txt file corresponding to one object in the image. Each row contains 5 elements: The object identifier, top left corner x coordinate, top left corner y coordinate, height, and width, separated by a whitespace. The object identifier represents good cans as 0 and defective cans as 1.</p> <h1>YOLOv4_object_detection.ipynb</h1> <p>This notebook was created to give the user a step-by-step tutorial on how to train a YOLOv4 algorithm with a custom dataset using a free GPU on Google Collab, the prerequisite to use it are:</p> <ul> <li>To have ready the dataset.</li> <li>Have the training txt file with the path to all images used for training.</li> <li>Have the test txt file with the path to all images used for testing.</li> </ul> <p>There are other requirements like cloning a GitHub repository and altering certain files on that repository; however, those steps are discussed within the notebook.</p> <p>At the end of the notebook an example on how to test the trained model with images and/or videos is shown, however since Google Collab doesn’t have access to the physical computer of the user live stream video is not part of the example.</p> <h1>deploy.py</h1> <p><em>Disclaimer: This code is not optimized, and its intended purpose is to teach students how to run YOLO on a raspberry pi using the OpenCV library.</em></p> <p>To use this code with different files or datasets, be sure to change the two parameters inside the net3 variable which are the cfg file used while training the algorithm and the weights file. You should also change the class list to include your classes, keeping in mind that the classes order must correspond to the order of the labeling process and class 0 is the first one on the list.</p> <p>Also to change the Title of the created image prompt you shout go to the line calling the imshow method and change the ‘Tiny YOLOv4’ string.</p> <p>This algorithm uses the first camera it finds and opens up a display image with the detected objects surrounded by a bounding box, on top of that box the top predicted class is going to show, to change color of bounding boxes or text change the rectangle method where it says GREEN as well as in the next code line ant change the number to change the thickness of the line.</p> <p>This code has a hardcoded confidence threshold for both the YOLO objectevness score and the class score, this can be found in the NMSBoxes method and the if confidence line accordingly. The main value to change first is the if confidence value.</p> <p>To close the image, you need to press the key ‘q’ as closing the display window is not going to work as it will reopen again.</p> <p>Note: This code allows the pop-up window, which displays the detections, to be closed only when the "q" key is pressed. Simply closing the window will not work.</p>
Transformers Model Zoos and Soups: A Population of Language and Vision Models
<p>Model Zoos submitted to the NeurIPS 2024 Dataset & Benchmark track: "<em>Transformer Model Zoos and Soups: A Population of Language and Vision Models</em>"</p> <p>We generate two model zoos, one for computer vision built on the ViT-S architecture, and one for language modeling based on the BERT architecture. For each, we train several backbone models with varying hyperparameters, and further fine-tune them using multiple hyperparameter combinations. We further annotate every model with performance metrics. These include test accuracy and F1-score, as well as the generalization gap. For the vision models, we also include the robust accuracy after a FGSM attack.</p>
Vision-Transformer, ViT, model validation dataset
<p><span>The U.S. cotton industry is highly concerned with removing plastic contamination from cotton lint. A major source of this </span><span>contamination is the plastic used to wrap cotton modules produced by John Deere round module harvesters. A machine-vision </span><span>detection and removal system has been developed to address this problem, using low-cost color cameras to detect plastic in the </span><span>cotton stream and remove it. However, the system requires a lot of calibration and is difficult for cotton gin workers to operate due to </span><span>its reliance on custom machine-vision classifier running on low-cost ARM computers running Linux. This research aims to make the system more user-friendly by adding an </span><span>auto-calibration feature that can track cotton colors and avoid plastic images, reducing the need for skilled personnel to operate the </span><span>system and making it easier for the cotton ginning industry to adopt. This image dataset was created to validate several Vision-</span><span>Transformer, ViT, AI models that in combination provides the key enabling technology for the auto-calibration code.</span></p>
ScienceDex guides
Understand access before you commit
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.