The data provided corresponds to the open-source codes and reference images used in a computer interface for real-time gait biofeedback using a Wearable Integrated Sensor System for Data Acquisition.The details of the system can be found in the study titled Computer interface for real-time gait biofeedback using a Wearable Integrated Sensor System for Data Acquisition, accepted for publication in the IEEE Transactions on Human-Machine Systems journal. 

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This dataset contains three benchmark datasets as part of the scholarly output of an ICDAR 2021 paper: 

Meng Ling, Jian Chen, Torsten Möller, Petra Isenberg, Tobias Isenberg, Michael Sedlmair, Robert S. Laramee, Han-Wei Shen, Jian Wu, and C. Lee Giles, Document Domain Randomization for Deep Learning Document Layout Extraction, 16th International Conference on Document Analysis and Recognition (ICDAR) 2021. September 5-10, Lausanne, Switzerland. 

This dataset contains nine class lables: abstract, algorithm, author, body text, caption, equation, figure, table, and title.

Instructions: 

Image files are in png formats and the metafiles are in plain text. 

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The dataset is divided into two sub-folders - 'source' and 'target'. The 'source' folder has a total of 4,080 images of Chest X-rays. The 'target' folder has a total of 4,080 Dual-Energy subtracted images corresponding to the images present in 'source' folder.

Instructions: 

Detailed documentation is provided in the following link: https://github.com/hmchuong/ML-BoneSuppression

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We have developed this dataset for the Bangla image caption. Here, we have recorded 500 images with one caption of each. Basically the lifestyle, festivals are mainly focused in this dataset. We have accomplished rice/harvest festivals, snake charming, palanquin, merry-go-round, slum, blacksmith, potter, fisherman, tat shilpo, jamdani, shutki chash, date juice, hal chash, tokai, pohela falgun, gaye holud, etc.

   

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Crowds express emotions as a collective individual, which is evident from the sounds that a crowd produces in particular events, e.g., collective booing, laughing or cheering in sports matches, movies, theaters, concerts, political demonstrations, and riots.

Instructions: 

Extract locally the zip files, read the readme file.

Instructions for dataset usage are included in the open access paper: Franzoni, V., Biondi, G., Milani, A., Emotional sounds of crowds: spectrogram-based analysis using deep learning (2020) Multimedia Tools and Applications, 79 (47-48), pp. 36063-36075. https://doi.org/10.1007/s11042-020-09428-x

File are released under Creative Commons Attribution-ShareAlike 4.0 International License

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The Magnetic Resonance – Computed Tomography (MR-CT) Jordan University Hospital (JUH) dataset has been collected after receiving Institutional Review Board (IRB) approval of the hospital and consent forms have been obtained from all patients. All procedures followed are consistent with the ethics of handling patients’ data.

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This is a collection of 2D and 3D images used for grayscale image processing tests. It includes at least 8 images of each of the following sizes:

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Accurate and efficient anomaly detection is a key enabler for the cognitive management of optical networks, but traditional anomaly detection algorithms are computationally complex and do not scale well with the amount of monitoring data. Therefore, this dataset enables research on new optical spectrum anomaly detection schemes that exploit computer vision and deep unsupervised learning to perform optical network monitoring relying only on constellation diagrams of received signals.

Instructions: 

The dataset contains a set of folders, each one representing one normal/anomalous case.

Within each folder, a number of .mat files contain the raw data collected from VPITransmissionMaker. The images folder contains the rendered constellation diagrams.

To render your own constellation diagrams, check the "generate_plots.m" file in the root folder.

More information on how to use in the GitHub repository.

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Given the difficulty to handle planetary data we provide downloadable files in PNG format from the missions Chang'E-3 and Chang'E-4. In addition to a set of scripts to do the conversion given a different PDS4 Dataset.

Instructions: 

Please see Readme inside ZIP files for more information about the provided data and scripts. 

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Existing datasets  for reflection symmetry detection contain shapes which are single contour shapes, thus they are not really challenging. We also need to consider how well a symmetry detector works on complex/compound shapes where traditional methods based on contour approach can not. On the other hand, there is only one symmetrical axes for every shape of this dataset. Therefore, this fails to evaluate how a symmetry detector works on a shape containing several symmetrical axis and how good the detection is when the number of symmetrical axis is unknown.

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