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26 results for “Visual Recognition”
Transposition confusability during visual word recognition
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MatSim Dataset and benchmark for one-shot visual materials and textures recognition
<p><strong>The MatSim Dataset and benchmark</strong></p> <p>Synthetic dataset and real images benchmark for visual similarity recognition of materials and textures.</p> <p>MatSim: a synthetic dataset, a benchmark, and a method for computer vision-based recognition of similarities and transitions between materials and textures focusing on identifying any material under any conditions using one or a few examples (one-shot learning).</p> <p>Based on the paper: <a href="https://arxiv.org/pdf/2212.00648.pdf">One-shot recognition of any material anywhere using contrastive learning with physics-based rendering</a></p> <p> </p> <p><strong>Benchmark_MATSIM.zip: </strong>contain the benchmark made of real-world images as described in the paper</p> <p><strong>Dataset Generation Scripts.zip: </strong>Contain the Blender (4.1) Python scripts used for generating the dataset<br><br><a href="https://zenodo.org/record/7390166/files/MatSim_object_train_split_1.zip?download=1"><strong>MatSim_object_train_split_1,2,3....zip:</strong> </a>Contain a subset of the synthetics dataset for images of CGI images materials on random objects as described in the paper.</p> <p><strong>MatSimTrainObjectsNearField_.zip </strong>Contain train sets with near fieldlight sources</p> <p><strong><a href="https://zenodo.org/record/7390166/files/MatSim_Vessels_Train_1.zip?download=1">MatSim_Vessels_Train_1,2,3....zip </a></strong><a href="https://zenodo.org/api/files/020f90b2-7c41-44ad-86e3-69257884a569/MatSim_object_train_split_1.zip"><strong>:</strong> </a>Contain a subset of the synthetics dataset for images of CGI images materials inside transparent containers as described in the paper.<br><br><strong>*Note: these are subsets of the dataset; the full dataset can be found at:</strong><br><a href="https://e1.pcloud.link/publink/show?code=kZIiSQZCYU5M4HOvnQykql9jxF4h0KiC5MX">https://e1.pcloud.link/publink/show?code=kZIiSQZCYU5M4HOvnQykql9jxF4h0KiC5MX</a></p> <p>or<br><a href="https://icedrive.net/s/A13FWzZ8V2aP9T4ufGQ1N3fBZxDF">https://icedrive.net/s/A13FWzZ8V2aP9T4ufGQ1N3fBZxDF</a></p> <p> </p>
Research Data: Facial Expression Recognition under Visual Field Restriction
<p>This dataset contains the following files:</p> <p><strong>-</strong> <strong>view_trial.xlsx:</strong> Excel spreadsheet containing data from individual trials.<br><strong>-</strong> <strong>view_participant.xlsx:</strong> Excel spreadsheet containing data aggregated at the participant level.<br><strong>- consensus.xlsx:</strong> Excel spreadsheet containing consensus data analysis.<br><strong>- image_id_list.txt:</strong> Text file listing the IDs of the images used in the study from The Karolinska Directed Emotional Faces (KDEF); https://kdef.se/.</p> <p>These files provide comprehensive data used in the research project titled "Exploring the Visual Field Restriction in the Recognition of Basic Facial Expressions: A Combined Eye Tracking and Gaze Contingency Study" conducted by M. B. Urtado, R. D. Rodrigues, and S. S. Fukusima. The dataset is intended for analysis and replication of the study's findings.</p> <p>Please, when using these data, we kindly request citing the following article:<br>Urtado, M.B.; Rodrigues, R.D.; Fukusima, S.S. <strong>Visual Field Restriction in the Recognition of Basic Facial Expressions: A Combined Eye Tracking and Gaze Contingency Study</strong>. <em>Behavioral Sciences</em> <strong>2024</strong>, <em>14</em>, 355. <a href="https://doi.org/10.3390/bs14050355">https://doi.org/10.3390/bs14050355</a></p> <p>The study was approved by the Research Ethics Committee (CEP) of the University of São Paulo (protocol code 41844720.5.0000.5407). </p>
Dataset for the manuscript "Attribute Recognition: A New Method for Grouping Planetary Images by Visual Characteristics, Using the Example of Mn-Rich Rocks in the Floor of Gale Crater, Mars."
<p>This dataset supports the manuscript "Attribute Recognition: A New Method for Grouping Planetary Images by Visual Characteristics, Using the Example of Mn-Rich Rocks in the Floor of Gale Crater, Mars." The dataset is contained in a single CSV file with 201 data rows (one row per NASA Curiosity rover ChemCam instrument target used in the study). The columns in this dataset include the martian solar day (sol) on which each target was imaged by ChemCam; the standoff distance from ChemCam to each target (in meters); binary columns (values are either 1 or 0, indicating presence or absence, respectively) for each of the 17 visual attributes we documented for each target image; the corresponding greyscale ChemCam RMI mosaic file location (on the Planetary Data System); and columns indicating which group each target was sorted into under each classification algorithm discussed in the text (P_{SG}: simple graph method; P_{AP}: automatic partitioning method; P_{\lambda=1.6}: community detection method with \lambda=1.6). To obtain the binary strings used for the classification algorithms, the 17 visual attribute columns can be concatenated. </p> <p>Also included is a collection of HTML files that enables easy viewing of the RMI mosaics in each cluster, using the Planetary Data System links. To use it, download the <code>.zip</code> file, unzip it, and open the <code>index.html</code> file in the browser of your choice (likely will work to simply double-click <code>index.html</code>)</p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - YOLOv5 Object Detection Dataset (Oxford-IIIT Pet)
<p>Preprocessed dataset for Oxford-IIIT Pet in YOLOv5 format.. Ground truth labels for head bounding boxes, body bounding boxes (derived from segmentation mask).</p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - YOLOv5 Breed Classification Dataset (Oxford-IIIT Pet)
<p>Oxford-IIIT Pet Dataset with ground truth labels for breeds (from https://public.roboflow.com/object-detection/oxford-pets).</p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - Kashtanka Pets (All Dev and Test Images, Single Folder)
<p>Kashtanka Pets images, with all Dev and Test images (total 66639 images). In a single folder, with filenames indicating path of file in original dataset distribution.</p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - YOLOv5 Object Detection Dataset (Tsinghua Dogs)
<p>Preprocessed dataset for Tsinghua Dogs in YOLOv5 format.. Ground truth labels for head bounding boxes, body bounding boxes</p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - YOLOv5 Breed Classification Dataset (Tsinghua Dogs)
<p>Tsinghua Dogs Dataset with ground truth labels for breeds in YOLOv5 format.</p>
Static visual predator recognition in jumping spiders
<p>Visually detecting, recognizing, and responding appropriately to predators increases survival. Failure to detect a predator or long decision times carry high and potentially fatal costs. Consequently, many animals show general anti-predatory responses toward threatening stimuli, e.g., looming objects. However, in the context of lurking or stalking ambush predators, visual recognition is based on static visual cues, making this task computationally demanding.</p> <p>Jumping spiders (Salticidae) have superb vision and are excellent ambush predators but they can equally fall prey to other jumping spiders. In a hierarchical decision-making setup, we tested whether the common zebra jumping spider (<em>Salticus scenicus</em>) can visually recognize stationary predators. We measured the spiders’ behavioural responses towards predator (naturally co-occurring, non-co-occurring and artificial) and non-predator objects as well as towards objects with modified features.</p> <p>Our experiments show that salticids demonstrate a robust, fast, and repeatable “freeze and retreat” behaviour when presented with stationary predators, but not similarly sized non-predator objects. Anti-predator responses were triggered by co-occurring and non-co-occurring salticid predators, as well as by 3D-printed salticid models (based on micro-CT scans), suggesting a generalized predator detection/classification. Using modified 3D-printed models, we found evidence that eyes act as an important cue. However, eyes alone did not explain the responses, suggesting that underlying processes rely on multiple rather than single features.</p> <p>To address the role of learning and memory, we tested newly emerged spiderlings and found the same behavioural responses towards predator objects suggesting an innate response. The ability of jumping spiders to innately recognize a non-moving threat is surprising in terms of underlying cognitive processes and the evolution thereof.</p> <p>Escaping from a predator before an attack has been launched likely carries sufficient selective benefits. From a cognitive perspective, the overlap of static visual characteristics between salticid predators, prey, and conspecifics invites further questions considering the mechanisms of such nuanced visual discrimination and categorization in animals with complex vision but relatively small nervous systems.</p>
Multi-Domain Dataset for Robots (MDDRobots) - Multi-Domain Indoor Dataset for Visual Place Recognition and Anomaly Detection by Mobile Robots
<h2><strong>License</strong></h2> <p>The MDDRobots dataset is made available under the CC BY 4.0 license <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>.</p> <h2><strong>Summary</strong></h2> <p>The Multi-Domain Dataset for Robots (MDDRobots) contains data for computer vision problems, indoor visual place recognition, and anomaly detection. The recorded images are from different cameras and indoor environmental conditions. </p> <p>It is obligatory to cite the following paper in every work that uses the dataset: <br><strong>Wozniak, P., Krzeszowski, T. & Kwolek, B. Multi-Domain Indoor Dataset for Visual Place Recognition and Anomaly Detection by Mobile Robots. <em>Sci Data</em> 12, 817 (2025). https://doi.org/10.1038/s41597-025-05124-3</strong></p> <h2><strong>Data description</strong></h2> <p>The data are divided into five sets (containing data for different cameras), which have further subsets. Each of the subsets: Training, Test 1, Test 2, and Test 3 consists of nine image sequences. A total of 89,550 three-channel RGB color images in PNG format are organized into 20 zip folders with a whole size of 34.3 GB. Each image in the sequence has a label that represents a room. The number of images for each subset differs due to the split into training and testing data. The difference also results from different methods of recording the image sequences. In order to have balanced data in the subsets, each room in the sequence has the same number of images. Different environmental changes were introduced in each subset. The data from Test 1 are closest to those from the training set. The differences between the sequences are mainly due to changes in the route, robot, and recording equipment. The rooms are well lighted, but not overexposed. The sequences from Test 3 present changed conditions, such as a different time of day, a changed lighting system, and intensive layout changes. The key change is the different paths of the human and the robot. This means a different perspective from previously recorded scenes. The Test 2 sequences pose the most difficult challenge because they contain various recorded activities performed by people moving around rooms. People can occlude important parts of the scene and pass in front of the camera. The images were anonymized by manually blurring the faces of observed people.</p> <h2><strong>Dataset structure<br></strong></h2> <ul> <li>RobotPiCamera_DataSet <ul> <li>DataSet_RobotPiCamera_RGB_train</li> <li>DataSet_RobotPiCamera_RGB_test1</li> <li>DataSet_RobotPiCamera_RGB_test2</li> <li>DataSet_RobotPiCamera_RGB_test3</li> </ul> </li> <li> Xtion_DataSet <ul> <li>DataSet_XTION_RGB_train</li> <li>DataSet_XTION_RGB_test1</li> <li>DataSet_XTION_RGB_test2</li> <li>DataSet_XTION_RGB_test3</li> </ul> </li> <li> GOPRO_DataSet <ul> <li>DataSet_GOPRO_RGB_train</li> <li>DataSet_GOPRO_RGB_test1</li> <li>DataSet_GOPRO_RGB_test2</li> <li>DataSet_GOPRO_RGB_test3</li> </ul> </li> <li>iPhone_DataSet <ul> <li>DataSet_IPHONE_RGB_train</li> <li>DataSet_IPHONE_RGB_test1</li> <li>DataSet_IPHONE_RGB_test2</li> <li>DataSet_IPHONE_RGB_test3</li> </ul> </li> <li>P40PRO_DataSet <ul> <li>DataSet_P40PRO_RGB_train</li> <li>DataSet_P40PRO_RGB_test1</li> <li>DataSet_P40PRO_RGB_test2</li> <li>DataSet_P40PRO_RGB_test3</li> </ul> </li> </ul> <p><em>Example folder content: DataSet_P40PRO_RGB_train\Corridor1_RGB - 00000000.png, 00000001.png, 00000002.png, 00000003.png, ... 00000599.png.</em></p> <p>Total Images (Images per Place)</p> <table> <tbody> <tr> <td>Subset</td> <td>Mounted</td> <td>Training</td> <td>Test 1</td> <td>Test 2</td> <td>Test 3</td> </tr> <tr> <td>Pi Camera</td> <td>Robot</td> <td>7200 (800)</td> <td>5400 (600)</td> <td>5400 (600)</td> <td>5400 (600)</td> </tr> <tr> <td>Xtion</td> <td>Robot</td> <td>7200 (800) </td> <td>1800 (200) </td> <td>1800 (200)</td> <td>1800 (200) </td> </tr> <tr> <td>GoPro</td> <td>Hand</td> <td>5400 (600)</td> <td>4500 (500)</td> <td>4500 (500)</td> <td>4500 (500)</td> </tr> <tr> <td>iPhone</td> <td>Hand</td> <td>5400 (600) </td> <td>4500 (500)</td> <td>4500 (500)</td> <td>4500 (500) </td> </tr> <tr> <td>P40Pro</td> <td>Hand</td> <td>5400 (600)</td> <td>4050 (450)</td> <td>3150 (350) </td> <td>3150 (350) </td> </tr> </tbody> </table> <h2><br>Further information</h2> <p>For any questions, comments or other issues please contact Piotr Woźniak <p.wozniak@prz.edu.pl>.</p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - Kashtanka Pets (400 Hand-labelled Images - Cats & Dogs, Single Folder)
<p>400 images (200 cats, 200 dogs) hand-labelled by Maria E. with head and body bounding box labels in YOLOv5 format. Images are in a single folder, no separate folders for cats and dogs.</p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - Kashtanka Pets (200 Hand-labelled Images, Cats and Dogs, Separate Folders)
<p>400 images (200 cats, 200 dogs) hand-labelled by Maria E. with head and body bounding box labels in YOLOv5 format. Images for cats, for dogs are in a separate folders.</p>
Supporting data (pre-processed) for: "Frequency-tagged visual evoked responses track syllable effects in visual word recognition"
<p>Pre-processed data. </p> <p>Data were re-referenced off-line to the average of left and right mastoid electrodes, bandpass filtered from 5 to 100 Hz (4th order Butterworth filter) and then segmented to include 200 ms before and 2000 ms after stimulus onset. Epoched data were normalized based on a prestimulus period of 200 ms, and then evaluated according to a sample-by-sample procedure to remove noisy sensors that were replaced using spherical splines. Additionally, EEG epochs that contained data samples exceeding threshold (100 uV) were excluded on a sensor-by-sensor basis, including horizontal and vertical eye channels</p> <p>The original data:<br> Montani, Veronica. (2019). Supporting data for: "Frequency-tagged visual evoked responses track syllable effects in visual word recognition" [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3260451</p>
Visual recognition and coevolutionary history drive responses of amphibians to an invasive predator
<p><span>During biotic invasions, native prey are abruptly exposed to novel predators and are faced with unprecedented predatory pressures. Under these circumstances, the lack of common evolutionary history may hamper predator recognition by native prey, undermining the expression of effective anti-predatory responses. Nonetheless, mechanisms allowing prey to overcome evolutionary naïveté exist. For instance, in naïve prey, history of coevolution with similar native predators or recognition of general traits characterizing predators can favor recognition of stimuli released by invasive predators. However, few studies assessed how these mechanisms shape prey response at the community level. Here, we evaluated behavioral responses in naïve larvae of 13 amphibian species to chemical and visual cues associated with an invasive predator, the American red swamp crayfish (<em>Procambarus clarkii</em>). Moreover, we investigated how variation among species responses was related to their coexistence with a similar native crayfish predator. Amphibian larvae altered their behavior in presence of visual stimuli of the alien crayfish, while chemical cues elicited feeble and contrasting behavioral shifts. Activity reduction was the most common and stronger response, whereas in some species we detected more heterogeneous strategies also involving distancing and rapid escape response. Interestingly, species sharing coevolutionary history with the native crayfish were able to finely tune their response to the invasive one, performing bursts to escape. These results suggest native prey can respond to invasive predators through recognition of generic risk cues (e.g., approaching large shapes), still the capability of modulating anti-predator strategies may also depend on their coevolutionary history with similar native predators. </span></p>
Single Neurons Responses During Visual Recognition in Epileptic Patients
ClinicalTrials.gov study NCT02877576. IPD Sharing: NO. Countries: 1. Publications: 1.
Novel Visual Education Tool to Improve Recognition and Reporting of Postpartum Urgent Maternal Warning Signs
ClinicalTrials.gov study NCT06912776. IPD Sharing: NO. Countries: 1. Publications: 3.
Visual recognition and coevolutionary history drive responses of amphibians to an invasive predator
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Pinpointing the neural signatures of single-exposure visual recognition memory
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Exposure to a novel predator induces visual predator recognition by naïve prey
<p>The 'life-dinner principle' posits that there is greater selection pressure on the species that have more to lose in an interaction. Thus, based on the asymmetry within predator-prey interactions there is an advantage for prey to learn quickly, especially in response to novel, introduced predators. Here we test the 'learned recognition' hypothesis that posits that naïve prey species' ability to recognize and respond to introduced predators can be induced through experience. We quantified the behavioural response of initially predator naïve burrowing bettongs (<i>Bettongia lesueur</i>) that had been living in the presence (for 8 - 15 months) and absence of an introduced predator (feral cats—<i>Felis catus</i>) to models of cats, a herbivore (rabbit (<i>Oryctolagus cuniculus</i>)), novel object (plastic bucket) and no object (control). We expected that if bettongs recognized cats as a threat they would be more wary in the presence of cat models than either rabbit models, buckets or the control. Bettongs living without predators did not modify their behaviour in response to the cat model, but spent more time cautiously approaching the rabbit model compared to the control. However, bettongs living with cats spent more time cautiously approaching the cat model compared to the rabbit, bucket and control. Our results are consistent with the learned recognition hypothesis which suggests that a predator-naïve prey species ability to recognize novel predators is inducible through experience. Our finding suggests that antipredator responses of reintroduced species could be improved prior to release by exposing them to predators under carefully controlled conditions.</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.