This is a small dataset as a part of huge dataset of breast cancer images. The images are mammograms. 

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The data made available are the simulations of a time-resolved Monte Carlo model for use in quantitative as well as qualitative analysis of different types of particle atmospheres.

Instructions: 

1. Set the geometry

2. Define the atmosphere

   2.1 Define the scattering profile of each type of particle in the atmosphere.

   2.2 Define the relative amount of each type of particle.

   2.2 Define the mean free path.

3. Define other test variables

   3.1 Temperature

   3.2 Refraction index (complex or real)

4. Run the simulations

 

5. With the data obtained, perform data analysis.

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Calibration datasets used in the article Standard Plenoptic Cameras Mapping to Camera Arrays and Calibration based on DLT. These datasets were acquired with a Lytro Illum camera using two calibration grids with different sizes: 8 × 6 grid of 211 × 159 mm (Big Pattern) with approximately 26.5 mm cells, and 20×20 grid of 121.5 × 122 mm (Small Pattern) with approximately 6.1 mm cells. Each dataset acquired is composed of 66 fully observable poses of the calibration pattern.

Instructions: 

The dataset is divided into the following zip files:

  • GD44M00145_WhiteImages: White image database of the Lytro Illum camera used to acquire the datasets.

  • Big Pattern 2D - Full: Calibration dataset with 66 poses of the big calibration grid.

  • Big Pattern 2D - Sample: Calibration dataset with 10 poses of the big calibration grid.

  • Big Pattern 2D - Sample Reduced: Calibration dataset with 5 poses of the big calibration grid.

  • Small Pattern 2D - Full: Calibration dataset with 66 poses of the small calibration grid.

  • Small Pattern 2D - Sample: Calibration dataset with 10 poses of the small calibration grid.

  • Small Pattern 2D - Sample Reduced: Calibration dataset with 5 poses of the small calibration grid.

  • Object: Objects dataset with the same acquisition conditions as the calibration datasets.

  • PlenCalCVPR2013Datasets: Lytro images used in the article for Lytro 1st generation calibration.

 

In order to obtain the lightfield associated with each image, you should read the Lytro raw image files (.lfp) using Dansereau's calibration toolbox (https://github.com/doda42/LFToolbox) and the white images provided here. The calibration of these datasets can be performed using the calibration toolbox provided in the article (http://www.isr.tecnico.ulisboa.pt/~nmonteiro/articles/plenoptic/tcsvt2019).

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Research on damage detection of road surfaces has been an active area of research, but most studies have focused so far on the detection of the presence of damages. However, in real-world scenarios, road managers need to clearly understand the type of damage and its extent in order to take effective action in advance or to allocate the necessary resources. Moreover, currently there are few uniform and openly available road damage datasets, leading to a lack of a common benchmark for road damage detection.

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1805 Views

Dataset for Telugu Handwritten Gunintam

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Addtional datasets for the jounal paper subimitted to IEEE Transactions on Computational Imaging, including self-captured light field microscopy datasets with lab-assembled LF microscope.

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An image dataset including five types of weather conditions (cloudy, sunny, foggy, rainy and snowy) was constructed.

 This dataset, called FWID, includes 4000 images for each weather category, leading to a total of 20000 images. 

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An image dataset including five types of weather conditions (cloudy, sunny, foggy, rainy and snowy) was constructed.

 This dataset, called FWID, includes 4000 images for each weather category, leading to a total of 20000 images. 

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137 Views

An image dataset including five types of weather conditions (cloudy, sunny, foggy, rainy and snowy) was constructed.

 This dataset, called FWID, includes 4000 images for each weather category, leading to a total of 20000 images. 

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1564 Views

The orchid flower dataset was selected from the northern part of Thailand. The dataset contains Thai native orchid flowers, and each class contains at least 20 samples. The orchid dataset including 52 species and the visual characteristics of the flower are varying in terms of shape, color, texture, size, and the other parts of the orchid plant like a leaf, inflorescence, roots, and surroundings. All images are taken from many devices such as a digital camera, a mobile phone, and other equipment. The orchids dataset contains 3,559 images from 52 categories.

Instructions: 

Download links:

Test - https://drive.google.com/open?id=1AGYAHLJFS4qiLyNLznHDKtWZx0d4RCK1

Train - https://drive.google.com/open?id=1AHwLH3-P8eWAXgXMs-FU2Ni6b2YMO5yY

 

This dataset is only for research purposes.

 

Please remember cited correctly the paper: "Orchids Classification Using Spatial Transformer Network with Adaptive Scaling"

 

BibTeX:

 

@inproceedings{sarachai2019orchids,

  title={Orchids Classification Using Spatial Transformer Network with Adaptive Scaling},

  author={Sarachai, Watcharin and Bootkrajang, Jakramate and Chaijaruwanich, Jeerayut and Somhom, Samerkae},

  booktitle={International Conference on Intelligent Data Engineering and Automated Learning – IDEAL 2019},

  pages={1--10},

  DOI={978-3-030-33607-3_1},

  year={2019},

  organization={Springer International Publishing}

}

 

 

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