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24 results for “Object recognition”
CURE-OR: Challenging Unreal and Real Environments for Object Recognition
<p>As one of the research directions at <a href="https://ghassanalregib.info/">OLIVES Lab @ Georgia Tech</a>, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (1.M images) object recognition dataset (<a href="https://github.com/olivesgatech/CURE-OR">CURE-OR</a>) which is among the most comprehensive datasets with controlled synthetic challenging conditions. In <a href="https://github.com/olivesgatech/CURE-OR">CURE-OR</a> dataset, there are 1,000,000 images of 100 objects with varying size, color, and texture, captured with multiple devices in different setups. The majority of images in the dataset were acquired with smartphones and tested with off-the-shelf applications to benchmark the recognition performance of devices and applications that are used in our daily lives. Please refer to our <a href="https://github.com/olivesgatech/CURE-OR">GitHub page</a> for code, papers, and more information. Some data specifications are provided below:</p> <p><strong>Image Name Format : </strong></p> <p>"backgroundID_deviceID_objectOrientationID_objectID_challengeType_challengeLevel.jpg"</p> <p><strong>Background ID: </strong></p> <p>1: White 2: Texture 1 - living room 3: Texture 2 - kitchen 4: 3D 1 - living room 5: 3D 2 – office</p> <p><strong>Object Orientation ID: </strong></p> <p>1: Front (0 º) 2: Left side (90 º) 3: Back (180 º) 4: Right side (270 º) 5: Top</p> <p><strong>Object ID:</strong></p> <p> 1-100</p> <p><strong>Challenge Type:</strong> </p> <p>No challenge 02: Resize 03: Underexposure 04: Overexposure 05: Gaussian blur 06: Contrast 07: Dirty lens 1 08: Dirty lens 2 09: Salt & pepper noise 10: Grayscale 11: Grayscale resize 12: Grayscale underexposure 13: Grayscale overexposure 14: Grayscale gaussian blur 15: Grayscale contrast 16: Grayscale dirty lens 1 17: Grayscale dirty lens 2 18: Grayscale salt & pepper noise</p> <p><strong>Challenge Level: </strong></p> <p>A number between [0, 5], where 0 indicates no challenge, 1 the least severe and 5 the most severe challenge. Challenge type 1 (no challenge) and 10 (grayscale) has a level of 0 only. Challenge types 2 (resize) and 11 (grayscale resize) has 4 levels (1 through 4). All other challenges have levels 1 to 5.</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 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>
The ORBIT (Object Recognition for Blind Image Training)-India Dataset
<div> <p>The ORBIT (Object Recognition for Blind Image Training) -India Dataset is a collection of 105,243 images of 76 commonly used objects, collected by 12 individuals in India who are blind or have low vision. This dataset is an "Indian subset" of the original ORBIT dataset [1, 2], which was collected in the UK and Canada. In contrast to the ORBIT dataset, which was created in a Global North, Western, and English-speaking context, the ORBIT-India dataset features images taken in a low-resource, non-English-speaking, Global South context, a home to 90% of the world’s population of people with blindness. Since it is easier for blind or low-vision individuals to gather high-quality data by recording videos, this dataset, like the ORBIT dataset, contains images (each sized 224x224) derived from 587 videos. These videos were taken by our data collectors from various parts of India using the Find My Things [3] Android app. Each data collector was asked to record eight videos of at least 10 objects of their choice. </p> </div> <div> <p>Collected between July and November 2023, this dataset represents a set of objects commonly used by people who are blind or have low vision in India, including earphones, talking watches, toothbrushes, and typical Indian household items like a belan (rolling pin), and a steel glass. These videos were taken in various settings of the data collectors' homes and workspaces using the Find My Things Android app. </p> </div> <div> <p>The image dataset is stored in the ‘Dataset’ folder, organized by folders assigned to each data collector (P1, P2, ...P12) who collected them. Each collector's folder includes sub-folders named with the object labels as provided by our data collectors. Within each object folder, there are two subfolders: ‘clean’ for images taken on clean surfaces and ‘clutter’ for images taken in cluttered environments where the objects are typically found. The annotations are saved inside a ‘Annotations’ folder containing a JSON file per video (e.g., P1--coffee mug--clean--231220_084852_coffee mug_224.json) that contains keys corresponding to all frames/images in that video (e.g., "P1--coffee mug--clean--231220_084852_coffee mug_224--000001.jpeg": {"object_not_present_issue": false, "pii_present_issue": false}, "P1--coffee mug--clean--231220_084852_coffee mug_224--000002.jpeg": {"object_not_present_issue": false, "pii_present_issue": false}, ...). The ‘object_not_present_issue’ key is True if the object is not present in the image, and the ‘pii_present_issue’ key is True, if there is a personally identifiable information (PII) present in the image. Note, all PII present in the images has been blurred to protect the identity and privacy of our data collectors. This dataset version was created by cropping images originally sized at 1080 × 1920; therefore, an unscaled version of the dataset will follow soon. </p> </div> <div> <p>This project was funded by the Engineering and Physical Sciences Research Council (EPSRC) Industrial ICASE Award with Microsoft Research UK Ltd. as the Industrial Project Partner. We would like to acknowledge and express our gratitude to our data collectors for their efforts and time invested in carefully collecting videos to build this dataset for their community. The dataset is designed for developing few-shot learning algorithms, aiming to support researchers and developers in advancing object-recognition systems. We are excited to share this dataset and would love to hear from you if and how you use this dataset. Please feel free to reach out if you have any questions, comments or suggestions. </p> </div> <div> <p>REFERENCES: </p> </div> <div> <ol> <li> <p>Daniela Massiceti, Lida Theodorou, Luisa Zintgraf, Matthew Tobias Harris, Simone Stumpf, Cecily Morrison, Edward Cutrell, and Katja Hofmann. 2021. ORBIT: A real-world few-shot dataset for teachable object recognition collected from people who are blind or low vision. DOI: <a href="https://doi.org/10.25383/city.14294597" target="_blank" rel="noreferrer noopener">https://doi.org/10.25383/city.14294597</a></p> </li> <li> <p>microsoft/ORBIT-Dataset. <a href="https://github.com/microsoft/ORBIT-Dataset" target="_blank" rel="noreferrer noopener">https://github.com/microsoft/ORBIT-Dataset</a> </p> </li> <li> <p>Linda Yilin Wen, Cecily Morrison, Martin Grayson, Rita Faia Marques, Daniela Massiceti, Camilla Longden, and Edward Cutrell. 2024. Find My Things: Personalized Accessibility through Teachable AI for People who are Blind or Low Vision. In Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems (CHI EA '24). Association for Computing Machinery, New York, NY, USA, Article 403, 1–6. <a href="https://doi.org/10.1145/3613905.3648641" target="_blank" rel="noreferrer noopener">https://doi.org/10.1145/3613905.3648641</a> </p> </li> </ol> </div>
YCB-M: A Multi-Camera RGB-D Dataset for Object Recognition and 6DoF Pose Estimation
<p>While a great variety of 3D cameras have been introduced in recent years, most publicly available datasets for object recognition and pose estimation focus on one single camera. This dataset consists of 32 scenes that have been captured by 7 different 3D cameras, totaling 49,294 frames. This allows evaluating the sensitivity of pose estimation algorithms to the specifics of the used camera and the development of more robust algorithms that are more independent of the camera model. Vice versa, our dataset enables researchers to perform a quantitative comparison of the data from several different cameras and depth sensing technologies and evaluate their algorithms before selecting a camera for their specific task. The scenes in our dataset contain 20 different objects from the common benchmark YCB object and model set. We provide full ground truth 6DoF poses for each object, per-pixel segmentation, 2D and 3D bounding boxes and a measure of the amount of occlusion of each object.</p> <p>If you use this dataset in your research, please cite the following publication:</p> <p>T. Grenzdörffer, M. Günther, and J. Hertzberg, “YCB-M: A Multi-Camera RGB-D Dataset for Object Recognition and 6DoF Pose Estimation,” in <em>2020 IEEE International Conference on Robotics and Automation, ICRA 2020, Paris, France, May 31-June 4, 2020</em>. IEEE, 2020.</p> <pre><code>@InProceedings{Grenzdoerffer2020ycbm, title = {{YCB-M}: A Multi-Camera {RGB-D} Dataset for Object Recognition and {6DoF} Pose Estimation}, author = {Grenzd{\"{o}}rffer, Till and G{\"{u}}nther, Martin and Hertzberg, Joachim}, booktitle = {2020 {IEEE} International Conference on Robotics and Automation, {ICRA} 2020, Paris, France, May 31-June 4, 2020}, year = {2020}, publisher = {{IEEE}} }</code></pre> <p>This paper is also available on arXiv: <a href="https://arxiv.org/abs/2004.11657">https://arxiv.org/abs/2004.11657</a></p> <p> </p> <p>To visualize the dataset, follow these instructions (tested on Ubuntu Xenial 16.04):</p> <pre><code class="language-bash"># IMPORTANT: the ROS setup.bash must NOT be sourced, otherwise the following error occurs: # ImportError: /opt/ros/kinetic/lib/python2.7/dist-packages/cv2.so: undefined symbol: PyCObject_Type # nvdu requires Python 3.5 or 3.6 sudo add-apt-repository -y ppa:deadsnakes/ppa # to get python3.6 on Ubuntu Xenial sudo apt-get update sudo apt-get install -y python3.6 libsm6 libxext6 libxrender1 python-virtualenv python-pip # create a new virtual environment virtualenv -p python3.6 venv_nvdu cd venv_nvdu/ source bin/activate # clone our fork of NVIDIA's Dataset Utilities that incorporates some essential fixes pip install -e 'git+https://github.com/mintar/Dataset_Utilities.git#egg=nvdu' # download and transform the meshes # (alternatively, unzip the meshes contained in the dataset # to <path to venv_nvdu>/lib/python3.6/site-packages/nvdu/data/ycb/aligned_cm) nvdu_ycb -s # run nvdu_viz to visualize the dataset cd <a subdirectory of the YCB-M dataset with some frames> nvdu_viz --name_filters '*.jpg' </code></pre> <p>For further details, see README.md.</p>
Data from: Automatic patient-level recognition of four Plasmodium species on thin blood smear by a Real Time Detector Transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation
<p>Automatic patient-level recognition of four <em>Plasmodium</em> species on thin blood smear by a Real Time Dectector Transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation</p> <p>Emilie Guemas, Baptiste Routier, Théo Ghelfenstein-Ferreira, Camille Cordier, Sophie Hartuis, Bénédicte Marion, Sébastien Bertout, Emmanuelle Varlet-Marie, Damien Costa, Grégoire Pasquier</p> <p><strong>Abstract:</strong></p> <p>Malaria remains a global health problem with 247 million cases and 619,000 deaths in 2021. Diagnostic of <em>Plasmodium</em> species is important for administering the appropriate treatment. The gold-standard diagnosis from accurate species identification remains the thin blood smear. Nevertheless, this method is time-consuming and requires highly skilled and trained microscopists. To overcome these issues, new diagnostic tools based on deep learning are emerging. This study aimed to evaluate the performances of a RT-DETR (Real-Time Detection Transformer)object detection algorithm to discriminate <em>Plasmodium</em> species on thin blood smears images. The algorithm was trained and validated on a dataset consisting in 24,720 images from 475 thin blood smears corresponding to 2,002,597 labels. Performances were calculated with a test dataset of 4,508 images from 170 smears corresponding to 358,825labels coming from six French university hospital. At the patient level, the RT-DETR algorithm exhibited an overall accuracy of 79.4% (135/170) with a recall of 74% (40/54) and 81.9% (95/116) for negative and positive smears, respectively. Among <em>Plasmodium </em>positive smears, the global sensitivity was 82.7% (91/110) with a sensitivity of 90% (38/42), 81.8% (18/22) and 76.1% (35/46) for <em>P. falciparum</em>, <em>P. malariae </em>and <em>P. ovale/vivax,</em> respectively. The YOLOv5 model achieved a World Health Organization (WHO) competence level 2 for species identification. Besides, the RT-DETR algorithm may be run in real-time on low-cost devices such as a smartphone and could be suitable for deployment in low-resource setting areas where microscopy experts are lacking.</p> <p><strong>Data collection:</strong></p> <p>The training and validation dataset included 24,720 pictures taken from 475 manually May Grunwald-Giemsa (MGG)-stained thin blood smears from the Montpellier University Hospital collection and for a smaller part from the Toulouse University Hospital collection. In Montpellier, the pictures were taken with a Flexcam C1 microscope camera (Leica) attached to a Leica DM 2000 microscope and Leica DF450C microscope camera adapted with a Leica DM2500 microscope at X1000 magnification. Labelling of pictures was performed manually, and then automatically with manual correction with a Computer Visual Annotation Tools (CVAT) free software. Nine categories of labels were used: white blood cells (n=3,338), red blood cells (n=1,887,781), platelets (n=48,520), <em>Trypanosoma brucei </em>(n=2,773), and red blood cells infected by <em>P. falciparum </em>(n=43,545), <em>P. ovale </em>(n=4,651), <em>P. vivax </em>(n=4,115), <em>P. malariae </em>(n=2,849) and <em>Babesia divergens</em> (n=5,142).</p> <p>The test dataset included 4,508 pictures taken from 170 thin blood smears from the same number of patients from the Parasitology laboratories of University Hospitals of Montpellier, Toulouse, Rouen, Lille, Nantes and Saint-Louis in Paris (Table 1). Among these 170 patients, 54 were not infected, including two patients with Howell-Jolly bodies, and 116 were infected with hematozoa. For each patient, between 20 and 30 photos were taken from one thin blood smear with at least one hematozoan parasite per picture for infected patients.</p> <p>Accurate species diagnostic was made by a senior parasitologist, and for recent smears, it was confirmed by specific PCR, either performed locally (Toulouse) or at the Malaria French National Reference Center (Montpellier, Saint Louis, Rouen, Lille, Nantes).</p>
Data from: Evidence for normal novel object recognition abilities in developmental prosopagnosia
<p>The issue of the face specificity of recognition deficits in developmental prosopagnosia (DP) is fundamental to the organisation of high-level visual memory and has been increasingly debated in recent years. Previous DP investigations have found some evidence of object recognition impairments, but have almost exclusively used familiar objects (e.g., cars), where performance may depend on acquired object-specific experience and related visual expertise. An object recognition test not influenced by experience could provide a better, less contaminated measure of DPs' object recognition abilities. To investigate this, in the current study we tested 30 DPs and 30 matched controls on a novel object memory test (NOMT Ziggerins) and the Cambridge Face Memory Test (CFMT). DPs were impaired on the CFMT but showed no differences in accuracy or reaction times to controls on the NOMT. We found similar results when comparing DPs to a larger sample of 274 web-based controls. Additional individual analyses demonstrated that the rates of object recognition impairment in DPs did not differ from the rate of impairment in either control group. Together, these results demonstrate unimpaired object recognition in DPs for a class of novel objects that serves as a powerful index for broader novel object recognition capacity.</p>
FEater dataset: A molecular fragment dataset to benchmark the robustness of 3D flexible object recognition
<p>This dataset is associated with the work: Benchmarking the robustness of the correct identification of flexible 3D objects using common machine learning models</p> <pre><code># Original FEater-Single and FEater_Dual dataset. FEater_Single ├── TestSet_coord.h5 ├── TrainingSet_coord.h5 └── ValidationSet_coord.h5 FEater_Dual ├── TestSet_coord.h5 ├── TrainingSet_coord.h5 └── ValidationSet_coord.h5 # Non-redundant baseline dataset FEater_Baseline ├── TestSet_Dual.h5 ├── TestSet_Single.h5 ├── TrainingSet_Dual.h5 └── TrainingSet_Single.h5 # FEater-Single and FEater_Dual in different sample size FEater_Mini200 ├── Mini200_Dual.h5 └── Mini200_Single.h5 FEater_Mini400 ├── Mini400_Dual.h5 └── Mini400_Single.h5 FEater_Mini800 ├── Mini800_Dual.h5 └── Mini800_Single.h5</code></pre> <p>For further details of the usage, please visit the original GitHub repository: <a title="FEater_repo" href="https://github.com/miemiemmmm/FEater" target="_blank" rel="noopener">https://github.com/miemiemmmm/FEater</a></p>
Object Shape Recognition and Targeting Using 4x4 Ultrasonic Sensor Array
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Data from: Evidence for normal novel object recognition abilities in developmental prosopagnosia
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Technology Assisted Solutions for the Recognition of Objective Physiological Indicators of Post-Coronavirus-19 Fatigue
ClinicalTrials.gov study NCT05027724. IPD Sharing: NO. Countries: 1. Publications: 2.
Effects of Transcranial Magnetic Stimulation on Object Recognition
ClinicalTrials.gov study NCT01747200. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Data for Radial arm maze tests and Novel object recognition test
<p class="MsoNormal"><strong><span>Background</span></strong></p> <p class="MsoNormal"><span>Aging population is rapidly expanding worldwide, and age-related cognitive impairments proves detrimental for achieving a better productive and quality of life. Lack of effective therapies for age-related cognitive impairment focuses attention on developing preventive strategies, such as nutritional interventions, cell therapies and environmental manipulations. The objective of present study was to explore the comparative benefits of potential memory-enhancing strategies like supplementation of choline and docosahexaenoic acid (DHA) or administration of conditioned media derived from human embryonic kidney stem cells (HEK-CM) or exposure to environmental enrichment (EE), that attenuates cognitive impairments in aging mice. </span></p> <p class="MsoNormal"><strong><span>Results</span></strong></p> <p class="MsoNormal"><span>Spatial memory and cognition were decreased in normal aging mice. Aged mice exposed to dietary Ch-DHA or HEK-CM showed significant enhancement in spatial learning tasks, memory and cognition compared to the same in age-matched NAC mice. Ch-DHA and HEK-CM treated mice committed significantly lesser reference memory errors and attained a higher percentage of correct choices in spatial learning and memory tasks. Moreover, on testing for cognition in</span><span> NORT,</span><span> significantly higher number of visits to the novel object was observed in Ch-DHA supplemented and HEK-CM administered aging mice whereas HEK-CM and EE mice groups showed significantly greater number of visits to familiar object, when compared to same in age-matched NAC and HI-HEKCM groups respectively</span><span>.</span><span> </span></p> <p class="MsoNormal"><strong><span>Conclusion</span></strong></p> <p class="MsoNormal"><span>Supplementation of Ch-DHA and HEK CM treatment strategies have a higher potential [~ 20-30%] for enhancing spatial learning, memory and cognition in normal aged mice, whereas exposure to enriched environment seems to enhance only their short term memory.</span></p>
Data from: Object recognition and localization from 3D point clouds by maximum-likelihood estimation
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Data from: Disruptive camouflage impairs object recognition
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Data for Radial arm maze tests and Novel object recognition test
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RNA-Seq data from cortex and hippocampus (post novel object recognition test) of rats in a vagus nerve stimulation study
GEO Series GSE134309. Rattus norvegicus. 15 samples. Type: Expression profiling by high throughput sequencing.
A brain-inspired architecture for cost-efficient object recognition in shallow neural networks
<p>Demo codes for "A brain-inspired architecture for cost-efficient object recognition in shallow neural networks"</p> <p><strong>1. System requirements</strong><br> - MATLAB (2019a or 2019b is recommended.)</p> <p>- Installation of Deep Learning Toolbox</p> <p>- Uploaded codes were tested using MATLAB 2019a and 2019b.</p> <p>- No non-standard hardware is required to run the codes.</p> <p><br> <strong>2. Installation guide</strong><br> - Download "LRC_code.zip" and unzip the file.</p> <p>- Download "DATASET_ori.zip","pretrainedNet.zip" and unzip on same file.</p> <p>- Choose the proper subfolder of 'fun_modifed_toolbox' which is matched to your MATLAB version and<br> Move each file to the proper directory<br> (see the comment in line 4 of each file for proper directory e.g. 'dir = C:\Program Files\MATLAB\...')<br> </p> <p><strong>3. Instructions for use</strong><br> - By running "Main.m" and selecting code option and figure option (see line 35 - 41 on the Main.m)<br> code options :<br> i) flg_ShowRes : Show the result figure of demo code using the pretrained network (in pretrainedNet.zip)<br> ii) flg_Demo : Run the demo code using a randomly initialized network<br> figure options : flg1 - Result 1 / flg2 - Result 2 / flg3 - Result 3 </p> <p> </p> <p><strong>cf. Modified MNIST datasets</strong></p> <p>To separately examine the contribution of high and low-frequency information contained in sample images, we designed three types of modified MNIST datasets. Details are as follows:</p> <p><strong>Type 1</strong><em>: shape</em>. The “<em>shape</em>” dataset was designed by arranging a hand-written digit of 8 x 8 pixels in the center of a 28 x 28 pixels image. The dataset consists of eight categories depending on the number in the center (1 to 8). For this dataset, only local information (shape) of the digits is required for classification.</p> <p><br> <strong>Type 2</strong>: <em>position</em>. The “<em>position</em>” dataset was made by the following procedure. First, two digits of 8 x 8 pixels were randomly chosen. Second, these two digits were allocated in a 28 x 28 image, with one of the following position alignments: horizontal (top, middle, bottom), vertical (left, middle, right), or diagonal (45°, 135°). This dataset also consists of eight categories depending on the position only where the digits are located. Note that the shape of each number is irrelevant for classification.</p> <p><br> <strong>Type 3</strong>: <em>shape-position</em>. The “<em>shape-position</em>” dataset was made by the following procedure. First, a 28 x 28 pixel area was divided into four 14 x 14 areas. Second, two diagonally aligned areas were selected (either 45° or 135°). Third, one of two digits, either “7” or “9”, composed of 8 x 8 pixels was inserted into each selected area. This dataset consists of eight categories depending on both the shape and position of the digits. The reason we chose “7” and “9” among ten numbers is to adjust the difficulty of the task to be similar to that of the previous tasks. Note that this dataset requires both local information (shape) and global information (position) of the digits for classification. </p>
data set related to article Visuo-haptic transfer for object recognition in children with periventricular leukomalacia and bilateral cerebral palsy
<p>This record contains raw data related to article Visuo-haptic transfer for object recognition in children with periventricular leukomalacia and bilateral cerebral palsy</p>
Action recognition and object detection dataset for firearm-related actions
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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.