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We collected fundus photos from the Kangnam Sacred Heart Hospital, Hallym University School of Medicine, Seoul, South Korea (IRB approval number: 2022-10-026), that were obtained from 2000 to September 2022. The fundus photographs were taken by five skilled examiners using the KOWA Nonmyd 8S Fundus Camera (KOWA company, Japan). Among the 2,000 images, we chose 50 test images that were characterized as ``bad-quality'' due to one of the following reasons: media opacity, small pupil, or poor patient cooperation.

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This dataset consists of 462 field of views of Giemsa(dye)-stained and field(dye)-stained thin blood smear images acquired using an iPhone 10 mobile phone with a 12MP camera. The phone was attached to an Olympus microscope with 1000× objective lens. Half of the acquired images are red blood cells with a normal morphology and the other half have a Rouleaux formation morphology.

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This dataset comprises a diverse array of image files, each captured using either a mobile phone or a camera. The primary subject of these images is experiment reports, reflecting a wide range of experimental scenarios. These images have been taken in various environments, showcasing the flexibility of the dataset in accommodating different shooting conditions. Formatted as JPG documents, the images exhibit variations in size, offering a rich diversity for analysis.

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Nasal cytology is a medicine field that focuses on the examination of nasal mucosa cells with the objective of recognizing changes in the epithelium, which is frequently subjected to acute or chronic irritation and inflammation caused by viruses, bacteria, or fungi; in the last decade, nasal cytology is becoming increasingly critical in diagnosing nasal conditions.

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Recognizing and categorizing banknotes is a crucial task, especially for individuals with visual impairments. It plays a vital role in assisting them with everyday financial transactions, such as making purchases or accessing their workplaces or educational institutions. The primary objectives for creating this dataset were as follows:

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This data package(.zip) includes data of follower robots'  motion track、errors  and velocities in five simulatd experimental cases:(1),(2): set obstacle range on 0.8m and 1.0m,2 groups;(3):  oneside situation,  and the number of follower robots rises to 5.   (4):complex environment, which we place more obstacles.   (5): change the lead-follower formation  (6),(7):two types of formation tracks, circle and straight line,compare follower1,2.

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This dataset is a face dataset collected for the purpose of a face anti-spoofing study.

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Biometric management and that to which uses face, is indeed a very challenging work and requires a dedicated dataset which imbibes in it variations in pose, emotion and even occlusions. The Current work aims at delivering a dataset for training and testing purposes.SJB Face dataset is one such Indian face image dataset, which can be used to recognize faces. SJB Face dataset contains face images which were collected from digital camera. The face dataset collected has certain conditions such as different pose, Expressions, face partially occluded and with a uniform attire.

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Retail Gaze, a dataset for remote gaze estimation in real-world retail environments. Retail Gaze is composed of 3,922 images of individuals looking at products in a retail environment, with 12 camera capture angles.

Each image captures the third-person view of the customer and shelves. Location of the gaze point, the Bounding box of the person's head, segmentation masks of the gazed at product areas are provided as annotations.

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Holoscopic micro-gesture recognition (HoMG) database was recorded using a holoscopic 3D camera, which have 3 conventional gestures from 40 participants under different settings and conditions. The principle of holoscopic 3D (H3D) imaging mimics fly’s eye technique that captures a true 3D optical model of the scene using a microlens array. For the purpose of H3D micro-gesture recognition. HoMG database has two subsets. The video subset has 960 videos and the image subset has 30635 images, while both have three type of microgestures (classes).

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