Image Processing
These are some graphs that record the human ocular electrical signals and ocular impedance signals, each image from top to bottom is a time-frequency graph of the EOG, the EOG signals, the time-frequency graph of the impedance signals, the impedance signals, and the impedance signals, respectively. This dataset is used to train the eye movement detection model.
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This is the video dataset for SFDM paper. Only the first 30 seconds are to be used. The last 10 seconds are extended so that the trimming of the video does not make each clip end abruptly before 30 seconds.
File naming style: {case study number}{sequence}{clip number}{opt: falsely detected as other sequence}.webm
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We introduce a new image dataset named FabricDefect, which focuses on the warp and weft defects of cotton fabric. The images in the FabricDefect dataset were manually collected by several experienced fabric inspectors using a high-definition image acquisition system set up on an industrial fabric inspection machine. The sample collection process lasted for three months, with daily sampling from 6 a.m. to 8 p.m., covering various weather conditions and external lighting scenarios. All images were meticulously gathered according to predefined standards.
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This dataset is collected from Kaggle (https://www.kaggle.com/datasets/nazmul0087/ct-kidney-dataset-normal-cyst-tumor-and-stone ). The dataset was collected from PACS (Picture archiving and communication system) from different hospitals in Dhaka, Bangladesh where patients were already diagnosed with having a kidney tumor, cyst, normal or stone findings.
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This dataset (samplePointsCities_20240811_harmonized.csv) was used for the Rescaled Water Index (RWI) proposal. The GitHub page (https://github.com/edujusti/Rescaled-Water-Index-RWI) contains the Python and JavaScript (Google Earth Engine) scripts used for data production, statistical analyses, and result visualization of the RWI spectral index.
This spectral index is a modification of MNDWI to enhance the mappings of water surfaces.
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This dataset contains still thermal frames from thirty patients undergoing awake craniotomy for brain tumor resection. The data were used as part of a study on automated craniotomy masking, where the portion of the craniotomy image which contans the brain is identified. The data contains manually generated gold-standard masks, as well as masks created with the proposed method in "Automated Craniotomy Masking for Intraoperative Thermography".
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The ultrasound video data were collected from two sets of neck ultrasound videos of ten healthy subjects at the Ultrasound Department of Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine. Each subject included video files of two groups of LSCM, LSSCap, RSCM, and RSSCap. The video format is avi.
The MRI training data were sourced from three hospitals: Longhua Hospital, Shanghai University of Traditional Chinese Medicine; Huadong Hospital, Fudan University; and Shenzhen Traditional Chinese Medicine Hospital.
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This dataset comprises high-resolution imaging data of biological porcine, clinically approved porcine and bovine, and chick embryo heart tissues. The dataset includes comprehensive anatomical and structural details, making it valuable for research in cardiovascular biology, tissue engineering, and computational modeling. The porcine and bovine heart samples are clinically approved, ensuring relevance for translational and preclinical studies. The chick embryo heart data provides insights into early cardiac development.
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These are tight pedestrian masks for the thermal images present in the KAIST Multispectral pedestrian dataset, available at https://soonminhwang.github.io/rgbt-ped-detection/
Both the thermal images themselves as well as the original annotations are a part of the parent dataset. Using the annotation files provided by the authors, we develop the binary segmentation masks for the pedestrians, using the Segment Anything Model from Meta.
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434 Views