segmentation
Arabic handwritten letters Dataset (AHLD) consists of 8,000 handwritten Arabic letter images of size 128x128 pixels, distributed into 28 classes (Arabic alphabets). This dataset is derived from processing 582 images, each containing several letters,
written by 15 individuals. The dataset creation involves a series of image processing operations: image acquisition, grayscale conversion, binarization, noise reduction, segmentation, normalization, skeletonization, and dataset labeling.
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Around from 12th century MODI script was used to write Indian languages as Marathi, Hindi, and Gujarati etc. It was used as administrative script from 17th century to mid of 19th century in Maharashtra state (India). At present, MODI script users are diminishing away, and countable persons can understand the MODI script. The preserved archaic historical MODI handwritten documents contained important and rare cultural, historic, and administrative kind of information which is usable in present-days.
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Anemia is a condition in which the oxygen-carrying capacity of red blood cells is insufficient to meet the body's physiological needs and affects billions of people worldwide. An early diagnosis of this disease could prevent the advancement of other disorders. Currently, traditional methods used to detect anemia consist of venipuncture, which requires a patient to frequently visit laboratories. Therefore, anemia diagnosis using noninvasive and cost effective methods is an open challenge.
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8380 Views
The dataset used in the paper "A Deep Learning Approach for Segmentation, Classification and Visualization of 3D High Frequency Ultrasound Images of Mouse Embryos" is provided here. It contains both the segmentation and classification images with manual labels.
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650 Views
Intracellular organelle networks such as the endoplasmic reticulum (ER) network and the mitochondrial network serve crucial physiological functions. Morphology of these networks plays critical roles in mediating their functions.Accurate image segmentation is required for analyzing morphology of these networks for applications such as disease diagnosis and drug discovery. Deep learning models have shown remarkable advantages in accurate and robust segmentation of these complex network structures.
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2787 Views
This dataset is used to develop an algorithm for automatic segmenting the collected signals. When machining a workpiece in a milling process, vibration signals can be recorded by a 3-axis accelerometer, which is attached on the spindle of a CNC milling machine. To segment the recorded signals, a moving window (0.5 sec) is applied to sample the vibration signals and manually labeled the corresponding modes, i.e. dry run or milling, of each window. To verify the algorithm, 3 types of operations are provided and recorded in csv format.
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Pathologic Myopia Challenge (PALM), as a part of the serial challenge iChallenge, is organized as a half day Challenge, a Satellite Event of the ISBI 2019 conference in Venice, Italy. The PALM challenge focuses on the investigation and development of algorithms associated with the diagnosis of Pathological Myopia (PM) and segmentation of lesions in fundus photos from PM patients. The goal of the challenge is to evaluate and compare automated algorithms for the detection of pathological myopia on a common dataset of retinal fundus images.
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2927 Views
This dataset is a companion to a paper, "Segmentation Convolutional Neural Networks for Automatic Crater Detection on Mars" by DeLatte et al. 2019. DOI link: http://dx.doi.org/10.1109/JSTARS.2019.2918302
These are the segmentation target files for the three targets described in the paper: solid filled, thicker edge, and thinner edge.
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2510 Views