images

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. 

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  • Artificial Intelligence
  • Last Updated On: 
    Sun, 05/24/2020 - 08:57

    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.

    43 views
  • Artificial Intelligence
  • Last Updated On: 
    Tue, 05/05/2020 - 17:01

    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.

    113 views
  • Computer Vision
  • Last Updated On: 
    Fri, 02/28/2020 - 11:47

    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.

    228 views
  • Artificial Intelligence
  • Last Updated On: 
    Thu, 05/07/2020 - 22:51

    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.

    374 views
  • Artificial Intelligence
  • Last Updated On: 
    Thu, 01/02/2020 - 23:16

    Smartphone has been one of the most popular digital devices in the past decades, with more than 300 million smartphones sold every quarter in the world wide. Most of the smartphone vendors, such as Apple, Huawei, Samsung, launch their new flagship smartphones every year. People use smartphone cameras to shoot selfie photos, film scenery or events, and record videos of family and friends. The specifications of smartphone camera and the quality of taken pictures are major criteria for consumer to select and buy smartphones.

    132 views
  • Image Processing
  • Last Updated On: 
    Sat, 11/30/2019 - 00:37

    Wide varieties of scripts are used in writing languages throughout the world. In a multiscript and multi-language environment, it is necessary to know the different scripts used in every part of a document to apply the appropriate document analysis algorithm. Consequently, several approaches for automatic script identification have been proposed in the literature, and can be broadly classified under two categories of techniques: those that are structure and visual appearance-based and those that are deep learning-based.

    53 views
  • Computer Vision
  • Last Updated On: 
    Fri, 10/25/2019 - 06:52

    This dataset contains the simulated results of the low frequency bent antenna. The data is gererated using HFSS and ADS

    72 views
  • Other
  • Last Updated On: 
    Thu, 10/03/2019 - 00:24

    This dataset is introduced in paper "Point-cloud-based place recognition using CNN feature extraction" by T. Sun, et al.

    This dataset is for place recognition.

    The dataset is collected using an omni stereo camera and a VLP-16 Velodyne LiDAR tied together and placed on a tripod.

    84 views
  • Computer Vision
  • Last Updated On: 
    Wed, 08/21/2019 - 08:27