Image Processing
Re-curated Breast Imaging Subset DDSM Dataset (RBIS-DDSM) is a curated version of 849 images from the CBIS-DDSM dataset available online with a permissive copyright license (CC-BY-SA 3.0). The CBIS-DDSM dataset is an improved version of the DDSM dataset. The authors of the CBIS-DDSM dataset attempted to improve the ground truth by applying simple image processing based methods to enhance the edges without any manual intervention from medical experts in order to segment and annotate masses. However, these annotations (segmentation maps) are inaccurate in most of the images.
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Part of the list-mode dataset for the simulation model
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This dataset consists of 2 types of images i.e Authentic and Tampered. There are a total of 1,389 Authentic images and 597 Tampered images. Authentic images are camera clicked images in raw form & tampered images are the one being edited by Adobe Photoshop and few mobile applications. Different types of forgery techniques like copy-move, splicing, color enhancement, resizing etc have been applied on the tampered images.
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This is a dataset regarding the creation of forgery images. The dataset consists of 1000 original and 3000 forgery images generated from the original images. The original images have been retrieved from publicly available repositories. Three different models have been used for creating the forgery images: cut-paste, copy-move, and erase-filling. Both pre-processing (sharpening, color enhancement, resizing, blurring, regulating exposure) and post-processing (sampling, rotation, masking) techniques have been considered for the generation of the forgery images.
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This publication contains three new datasets for scene-level sketch semantic segmentation task, namely SKY-Scene, TUB-Scene, and Freehand-Scene. SKY-Scene and TUB-Scene are synthetic datasets, where the scene layout templates were extracted from dataset SketchyScene, and the object components were adopted from dataset Sketchy. They are composed to include more sketch-specific characteristics, e.g., sparsity, abstractness, and diversity, to truly evaluate segmentation performance. Freehand-Scene are fifty real human-drawn scene sketches for practical test.
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Fifty human-drawn scene sketches with semantic annotation.
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A synthetic scene-level sketch dataset for sketch semantic segmentation task. The scene layout templates were extracted from dataset SketchyScene, and the object components were adopted from dataset TU-Berlin.
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A synthetic scene-level sketch dataset for sketch semantic segmentation task. The scene layout templates were extracted from dataset SketchyScene, and the object components were adopted from dataset Sketchy.
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Fecal microscopic data set is a set of fecal microscopic images, which is used in object detection task. The datasets are collected from the Sixth People’s Hospital of Chengdu (Sichuan Province, China). The samples were went flow diluted, stirred and placed, and imaged with a microscopic imaging system. The clearest 5 images were collected for each view of each sample with Tenengrad definition algorithm. The dataset we collected includes 10670 groups of views with 53350 jpg images. The Resolution of images are 1200×1600. There are 4 categories, RBCs, WBCs, Molds, and Pyocytes.
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