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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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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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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Features Extracted from BraTS 2012-2013

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This dataset contains the comparison results on the 'Euroc' public dataset of DVIO, VINS-Mono, and ROVIO.

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This dataset comes up as a benchmark dataset for machines to automatically recognizing the handwritten assamese digists (numerals) by extracting useful features by analyzing the structure. The Assamese language comprises of a total of 10 digits from 0 to 9. We have collected a total of 516 handwritten digits from 52 native assamese people irrespective of their age (12-86 years), gender, educational background etc. The digits are captured in .jpeg format using a paint mobile application developed by us which automatically saves the images in the internal storage of the mobile.

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An accurate and reliable image-based quantification system for blueberries may be useful for the automation of harvest management. It may also serve as the basis for controlling robotic harvesting systems. Quantification of blueberries from images is a challenging task due to occlusions, differences in size, illumination conditions and the irregular amount of blueberries that can be present in an image. This paper proposes the quantification per image and per batch of blueberries in the wild, using high definition images captured using a mobile device.

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The Contest: Goals and Organisation

 The 2019 Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS), the Johns Hopkins University (JHU), and the Intelligence Advanced Research Projects Activity (IARPA), aimed to promote research in semantic 3D reconstruction and stereo using machine intelligence and deep learning applied to satellite images.

Instructions: 
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