Computer Vision
The Extended Moulouya Bird Detection Dataset (E-Moulouya BDD) is a comprehensive collection of annotated images proposed for bird detection. The dataset is a combination of three datasets, namely the XMBA dataset, the Rest Birds Dataset, and the D-Birds Dataset, which were merged and cleaned up to provide a consistent and unified labeling format. The E-Moulouya BDD comprises 13,000 annotated images, labeled with a standardized format to ensure consistency across the dataset. The dataset is publicly available on IEEE DATAPORT, making it an ideal resource for use by the scientific community.
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The dataset consists of NumPy arrays for each alphabet in Indian Sign Language, excluding 'R'. The NumPy arrays denote the (x,y,z) coordinates of the skeletal points of the left and right hand (21 skeletal points each) for each alphabet. Each alphabet has 120 sequences, split into 30 frames each, giving 3600 .np files per alphabet, using MediaPipe.
The dataset is created on the basis of skeletal-point action recognition and key-point collection.
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When fuel materials for high-temperature gas-cooled nuclear reactors are quantification tested, significant analysis is required to establish their stability under various proposed accident scenarios, as well as to assess degradation over time. Typically, samples are examined by lab assistants trained to capture micrograph images used to analyze the degradation of a material. Analysis of these micrographs still require manual intervention which is time-consuming and can introduce human-error.
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Industrial parts overlay image dataset
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272 Views
The Dataset consists of two videos, one recorded with blindfold on and the other without blindfold recorded using a 1080p Intel RealSense depth camera. It contains the videos, images extracted using ffmpeg and processed video which is made of a video with skipped frames created using ffmpeg. The scope of the dataset is for machine vision purposes to allow for tasks such as instance segmentation. A hat fixed on the head of a blindfolded person is used to record walking activities.
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zip and czip format
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Human activity recognition, which involves recognizing human activities from sensor data, has drawn a lot of interest from researchers and practitioners as a result of the advent of smart homes, smart cities, and smart systems. Existing studies on activity recognition mostly concentrate on coarse-grained activities like walking and jumping, while fine-grained activities like eating and drinking are understudied because it is more difficult to recognize fine-grained activities than coarse-grained ones.
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This dataset contains approx. 5000 labeled images of marker on Smith Chart of Keysight VNA 9914a. Since smith chart contains "infinity" (although compensated in the device mathematically), the data from Smith Chart window pane is not apt for tuning the all-pole MW filters.
This dataset can be used to track the marker position while tuning the circuitary when considering Image Processing based Filter Tuning. Images are taken from various angles to ensure the robust behaviour and high detection accuracy.
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this is dataset for our paper: "Large-scale Benchmark for Uncooled Infrared Image Deblurring", submitted for IEEE SIgnal Processing Letters.
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images of a zebra board providing crafted plumblines
images of scenes containing plumblines
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