Computer Vision
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The dataset comprises image files of size 640 x 480 pixels for various grit sizes of Abrasive sheets. The data collected is raw. It can be used for analysis, which requires images for surface roughness. The dataset consists of a total of 8 different classes of surface coarseness. There are seven classes viz. P80, P120, P150, P220, P320, P400, P600 as per FEPA (Federation of European Producers of Abrasives) numbering system and one class viz. 60 as per ANSI (American National Standards Institute) standards numbering system for abrasive sheets.
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This dataset contains both the artificial and real flower images of bramble flowers. The real images were taken with a realsense D435 camera inside the West Virginia University greenhouse. All the flowers are annotated in YOLO format with bounding box and class name. The trained weights after training also have been provided. They can be used with the python script provided to detect the bramble flowers. Also the classifier can classify whether the flowers center is visible or hidden which will be helpful in precision pollination projects.
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This dataset includes input dynamics (keystroke, touch, and mouse), affect data (physiological measurements), video, and text data collected from research participants aged 6 and older. The dataset includes data from a diverse set of participants, identifying as Asian, White, Middle Eastern or North African, Black or African American, and Hispanic, Latino, or of Spanish origin). Additionally, participants represent both iOS and Android users.
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Although the vertical Chinese text recognition dataset presented by Yu is public, it is reproduced from the PosterErase dataset, collected from the e-commerce platform for the poster text erasing task, and does not contain the challenges from real application scenarios. Therefore, we establish a benchmark dataset (Vertical and Horizontal Text Recognition Dataset, WHU-VHTR) to promote in-depth research on STR. WHU-VHTR contained 23674 images annotated with line-level transcriptions, collecting from Google Street View and real urban scene images in China.
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This study presents an automated approach for the generation of graphs from hand-drawn electrical circuit diagrams, aiming to streamline the digitization process and enhance the efficiency of traditional circuit design methods. Leveraging image processing, computer vision algorithms, and machine learning techniques, the system accurately identifies and extracts circuit components, capturing spatial relationships and diverse drawing styles.
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OCD description. Cell lines A172 and U251: human glioblastoma; MCF7: human breast cancer; MRC5: human lung fibroblast; SCC25: human squamous cell carcinoma. Cultivation condition CTR: cells belonging to the control group - without the addition of chemotherapy; TMZ: cells treated with 50 μM temozolomide in some cultivation step.
Split
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LDRText is a large-scale and diverse dataset that suitable for scene text image super-resolution and recognition tasks
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Computer vision (CV) techniques help to perform non-destructive seed viability detection (SVD) for faster, more efficient and fairer results. However, the seed vigor dataset currently suffers from insufficient number of samples, data noise, and imbalance of positive and negative samples.
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In international contexts, natural scenes may include text in multiple languages. Especially, Latin and Arabic scene character image dataset is essential for training models to accurately detect and recognize text regions within real-world images. This is crucial for applications such as text translation, image search, content analysis, and autonomous vehicles that need to interpret text in different languages.
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