Image Processing
Dataset for segmentation of the defects on the surfaces of the military cartridge cases. The datasets with non-defective, defective and masked image classes of the defective cartridge cases.
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This open dataset is subject to CC BY-NC-SA 4.0 License. The dataset is intended for scientific research purposes and it cannot be used for commercial purposes. The authors encourage users to use it for public research and as a testbench for private research. Please note that any promotional/marketing material built upon this dataset should be backed by publicly available description of the work leading to the promotional/marketing claims.
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The dataset contains results of the paper being submitted.
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This repository contains code to apply the ESPER method to quasi-continuum models of biomolecules exhibiting multiple degrees of freedom, as described in Seitz et al. (2022, IEEE TCI). As inputs into ESPER, detailed instructions are also provided for generating custom synthetic datasets with increasing complexity to mirror known cryo-EM image attributes.
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Currency recognition and classification is one essential task to do. Both paper and coin currency play important role in transactions in everyday life. But provided there are many datasets available of paper currency, and very less datasets are available of coin currency. Coin currency recognition becomes important because even though the amount for which people do coin transactions is small but inaccuracy in recognition can lead to huge loss. Following are the objectives to create this dataset:
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Cryo-EM dataset of 80S ribosomes from yeast. This dataset has been described in Dashti et al. (2014, PNAS) "Trajectories of the ribosome as a Brownian nanomachine". In that study, a subset of the dataset was used to demonstrate the performance of a machine learning technique (now termed ManifoldEM) using manifold embedding to determine the energy landscape of a molecule. The dataset is re-analyzed in Seitz et al.
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This project investigates bias in automatic facial recognition (FR). Specifically, subjects are grouped into predefined subgroups based on gender, ethnicity, and age. We propose a novel image collection called Balanced Faces in the Wild (BFW), which is balanced across eight subgroups (i.e., 800 face images of 100 subjects, each with 25 face samples).
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The experimental data in this paper comes from the bamboo sticks provided by farmers who sell bamboo in Anji. We randomly grab less than 100 bamboo sticks and bundle them together. The heights of 5cm, 10cm, 15cm, and 20cm were taken from the front and left and right inclination to take pictures, screen clear and effective experimental data, and then use labelimg software to label them. The sparse bamboo stick samples collected were 600.
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data
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