Image Processing
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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![](https://ieee-dataport.org/sites/default/files/styles/3x2/public/tags/images/color-2174045_1280.png?itok=hQ444ipy)
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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Dataset for detecting faults during PCB manufacturing
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Today, the cameras are fixed everywhere, in streets, in vehicles, and in any public area. However, Analysis and extraction of information from images are required. Particularly, in autonomous vehicles and in smart applications that are developed to guide tourists. So, a large dataset of scene text images is an important and difficult factor in the extraction of textual information in natural images. It is the input to any computer vision system.
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Achieving digital intraoral impressions is a key step in orthodontic, implant, and repair. Compared with the complex and time-consuming traditional plaster impression method, the intraoral 3D scanner can obtain digital impressions in real time. However, because of the saliva, enamel, metallic denture, etc., the quality of the captured 2D image, which is used for feature measurement and 3D reconstruction, is usually degraded.
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