WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.

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WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.

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ALL-IDB (Acute Lymphoblastic Leukemia) Image Database for Image Processing

ALL-IDB dataset comprises of two subsets among them one subset has 260 segmented lymphocytes of them 130 belongs to the leukaemia and the remaining 130 belongs to the non leukaemuia class it requires only classification. second subset has around 108 non segmented blood images that belongs to the leukaemia and non leukaemia groups thus requires segmentation and classification.

 

 

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760 Views

This dataset is for date-fruit grading. It contains the grades of three types of dates: Ajwa (grade 1, grade 2, and grade 3), Mabroom (grade 1, grade 2, and grade 3), dried Sukkary (grade 1 and 2)

Instructions: 

This dataset contains images of three types of dates with their grades:

- Ajwah: grade 1, grade 2, and grade 3

- Mabroom: grade 1, grade 2, and grade 3

- Sukkary: grade 1 and grade 2

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Images of various foods, taken with different cameras and different lighting conditions. Images can be used to design and test Computer Vision techniques that can recognize foods and estimate their calories and nutrition.

Instructions: 

Please note that in its full view, the human thumb in each image is approximately 5 cm by 1.2 cm.

For more information, please see:

P. Pouladzadeh, A. Yassine, and S. Shirmohammadi, “FooDD: Food Detection Dataset for Calorie Measurement Using Food Images”, in New Trends in Image Analysis and Processing - ICIAP 2015 Workshops, V. Murino, E. Puppo, D. Sona, M. Cristani, and C. Sansone, Lecture Notes in Computer Science, Springer, Volume 9281, 2015, ISBN: 978-3-319-23221-8, pp 441-448. DOI: 10.1007/978-3-319-23222-5_54

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3540 Views

This is the data for paper "Environmental Context Prediction for Lower Limb Prostheses with Uncertainty Quantification" published on IEEE Transactions on Automation Science and Engineering, 2020. DOI: 10.1109/TASE.2020.2993399. For more details, please refer to https://research.ece.ncsu.edu/aros/paper-tase2020-lowerlimb. 

Instructions: 

Seven able-bodied subjects and one transtibial amputee participated in this study. Subject_001 to Subject_007 are able-bodied participants and Subject_008 is a transtibial amputee.

 

Each folder in the subject_xxx.zip file has one continuous session of data with the following items: 

1. folder named "rpi_frames": the frames collected from the lower limb camera. Frame rate: 10 frames per second. 

2. folder named "tobii_frames": the frames collected from the on-glasses camera. Frame rate: 10 frames per second. 

3. labels_fps10.mat: synchronized terrain labels, gaze from the eye-tracking glasses, GPS coordinates, and IMU signals. 

3.1 cam_time: the timestamps for the videos, GPS, gazes, and labeled terrains (unit: second). 10Hz

3.2 imu_time: the timestamps for the IMU sensors (unit: second). 40Hz.

3.3 GPS: the GPS coordinates (latitude, longitude)

3.4 rpi_FrameIds, tobii_FrameIds: the frame ID for the lower-limb and on-glasses cameras respectively. The ids indicate the filenames in "rpi_frames" and "tobii_frames" respectively. 

3.5 rpi_IMUs, tobii_IMUs: the imu signals from the two devices. Columns: (accel_x,accel_y,accel_z,gyro_x,gyro_y,gyro_z)

3.6 terrains: the type of terrains the subjects are current on. Six terrains: tile, brick, grass, cement, upstairs, downstairs. "undefined" and "unlabelled" can be regarded as the same kind of data that needs to be deprecated.

 

The following sessions were collected during busy hours (many pedestrians were around):

'subject_005/01', 

'subject_005/02'

'subject_006/01', 

'subject_006/02', 

'subject_007/01', 

'subject_007/02', 

The following sessions were collected during non-busy hours (few pedestrians were around):

'subject_005/03', 

'subject_005/04',

'subject_006/03', 

'subject_006/04',

'subject_007/03', 

'subject_007/04',

'subject_008/01',

'subject_008/02'

The other sessions were collected without specific collecting hours (e.g. busy or non-busy). 

For the following sessions, the data collection devices were not optimized (e.g. non-optimal brightness balance). Thus, we recommend to use these sessions as training or validation dataset but not as testing data.

'subject_001/02'

'subject_003/01'

'subject_003/02'

'subject_003/03'

'subject_004/01'

'subject_004/02'

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The nucleus and micronucleus images in this dataset are collected manually from Google images. Many of these images are in RGB color while a few of them are in grayscale. The dataset includes 148 nucleus images and 158 micronucleus images. The images are manually curated, cropped, and labeled into these two classes by a domain of experts in biology. The images have different sizes and different resolutions. The sizes and shapes for nucleuses and micronucleuses images differ from one image to another. Each image may contain one or more nucleus or micronucleus.

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131 Views

CUPSNBOTTLES is an object data set, recorded by a mobile service robot. There are 10 object classes, each with a varying number of samples. Additionally, there is a clutter class, containing samples where the object detector failed.

Instructions: 

Download and extract the ZIP file containing all files. There is python code available (under 'scripts') to easily load the data set. Other programming languages should also handle .jpg, .hdf and .csv files for easy access. For easy access with python, a pickle dump file has been added. This has no extra information compared to the .csv file.

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181 Views

This dataset contains the images used in the paper "Fine-tuning a pre-trained Convolutional Neural Network Model to translate American Sign Language in Real-time". M. E. Morocho Cayamcela and W. Lim, "Fine-tuning a pre-trained Convolutional Neural Network Model to translate American Sign Language in Real-time," 2019 International Conference on Computing, Networking and Communications (ICNC), Honolulu, HI, USA, 2019, pp. 100-104.

Instructions: 

The code is written for MATLAB. We used transfer learning using AlexNet and GoogLeNet as convolutional neural network (CNN) backbones.

In MATLAB, replace the directory path with yours. If you want to recognize other classes, just add the images from different classes on labeled folders.

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895 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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