Artificial Intelligence
Slow moving motions are mostly tackled by using the phase information of Synthetic Aperture Radar (SAR) images through Interferometric SAR (InSAR) approaches based on machine and deep learning. Nevertheless, to the best of our knowledge, there is no dataset adapted to machine learning approaches and targeting slow ground motion detections. With this dataset, we propose a new InSAR dataset for Slow SLIding areas DEtections (ISSLIDE) with machine learning. The dataset is composed of standardly processed interferograms and manual annotations created following geomorphologist strategies.
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The "ShrimpView: A Versatile Dataset for Shrimp Detection and Recognition" is a meticulously curated collection of 10,000 samples (each with 11 attributes) designed to facilitate the training of deep learning models for shrimp detection and classification. Each sample in this dataset is associated with an image and accompanied by 11 categorical attributes.
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The constructed Aoralscan3 tooth registration dataset includes 1667 samples for training, 156 samples for validation, and 176 samples for testing. Jaw models are generated from hospital patients by oral scanning. The ground truth of the relative pose of each tooth is generated by adding random jittering to the tooth models. For each tooth, ground truth relative pose information was generated by introducing random jittering to the tooth models. This dataset can be used for point cloud registration.
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The training set, validation set, and testing set in the constructed Shining3D tooth pose dataset contain 1689, 150, and 150 samples, respectively. Jaw models are generated from hospital patients by oral scanning. The ground truth of the relative pose of each tooth is generated by adding random jittering to the tooth models. For each tooth, ground truth relative pose information was generated by introducing random jittering to the tooth models.
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The Aoralscan3 dataset includes 1573, 244, and 244 videos for the corresponding sets. The uniform size of the images in the dataset is 640 × 480 pixels. LabelMe software is employed to accurately mark the boundary and classify the region of each tooth in the datasets. This dataset is usef for orthodontic treatment. which is one of the research direction of artificial intelligence using current images and previous 3D models to estimate the relative position of individual teeth before and after orthodontic treatment.
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This is a PART of the dataset used in our paper titled "Detecting Anomalous Robot Motion in Collaborative Robotic Manufacturing Systems".
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This is a PART of the dataset used in our paper titled "Detecting Anomalous Robot Motion in Collaborative Robotic Manufacturing Systems".
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The Shining3D dataset consists of 1866, 272, and 272 videos for the training, validation, and testing sets, respectively. The uniform size of the images in the dataset is 640 × 480 pixels. LabelMe software is employed to accurately mark the boundary and classify the region of each tooth in the datasets. This dataset is usef for orthodontic treatment. Orthodontic treatment monitoring, one of the research direction of artificial intelligence, involves using current images and previous 3D models to estimate the relative position of individual teeth before and after orthodontic treatment.
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558 Views
TANG FENG: IMAGE PROCESSING AND COMPUTER VISION.
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Different faults are experienced by a power system, particulary in transmission lines. In this dataset, the IEEE 5-Bus Model was used to different types of transmission line faults.
Indication of the label of the faults come from the time that the fault has been induced in the simulation.
This dataset aims to be utilized for machine learning algorithms, particularly in multi-class classification of the transmission line fault. In this simulation, each fault was induced at each transmission line one instance at a time during a certain period.
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1124 Views