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The is a dataset for indoor depth estimation that contains 1803 synchronized image triples (left, right color image and depth map), from 6 different scenes, including a library, some bookshelves, a conference room, a cafe, a study area, and a hallway. Among these images, 1740 high-quality ones are marked as high-quality imagery. The left view and the depth map are aligned and synchronized and can be used to evaluate monocular depth estimation models. Standard training/testing splits are provided.

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  • Computer Vision
  • Last Updated On: 
    Thu, 10/17/2019 - 20:30

    A paradigm dataset is constantly required for any characterization framework. As far as we could possibly know, no paradigmdataset exists for manually written characters of Telugu Aksharaalu content in open space until now. Telugu content (Telugu: తెలుగు లిపి, romanized: Telugu lipi), an abugida from the Brahmic group of contents, is utilized to compose the Telugu language, a Dravidian language spoken in the India of Andhra Pradesh and Telangana just a few other neighboring states. The Telugu content is generally utilized for composing Sanskrit writings.

    116 views
  • Computer Vision
  • Last Updated On: 
    Wed, 11/27/2019 - 07:22

    Simulated Disaster Victim dataset consists of images and video frames containing simulated human victims in cluttered scenes along with pixel-level annotated skin maps. The simulation was carried out in a controlled environment with due consideration towards the health of all the volunteers. To generate a real effect of a disaster, Fuller’s earth is used which is skin-friendly and does not cause harm to humans. It created an effect of disaster dust over the victims in different situations. The victims included one female and four male volunteers.

    348 views
  • Computer Vision
  • Last Updated On: 
    Wed, 09/04/2019 - 06:16

    The benchmark dataset  are consisted of 2,413 three-channel RGB images obtained from Google Earth satellite images and AID dataset.

    121 views
  • Geoscience and Remote Sensing
  • Last Updated On: 
    Tue, 07/30/2019 - 10:03

    Device Fingerprinting for Access Control over a Campus and Isolated Network

    Device Fingerprinting (DFP) is a technique to identify devices using Inter-Arrival Time (IAT) of packets and without using any other unique identifier. Our experiments include generating graphs of IATs of 100 packets and using Convolutional Neural Network on the generated graphs to identify a device. We did two experiments where the first experiment was on Raspberri Pi and other experiment was on crawdad dataset.

     

    First Experiment: Raspberry Pi

    141 views
  • Communications
  • Last Updated On: 
    Sat, 01/26/2019 - 09:08

    After a hurricane, damage assessment is critical to emergency managers and first responders so that resources can be planned and allocated appropriately. One way to gauge the damage extent is to detect and quantify the number of damaged buildings, which is traditionally done through driving around the affected area. This process can be labor intensive and time-consuming. In this paper, utilizing the availability and readiness of satellite imagery, we propose to improve the efficiency and accuracy of damage detection via image classification algorithms.

    400 views
  • Geoscience and Remote Sensing
  • Last Updated On: 
    Thu, 12/13/2018 - 03:04

    A quantitative understanding of how sensory signals are transformed into motor outputs places useful constraints on brain function and helps reveal the brain's underlying computations. Here we present over 8,000 animal hours of behavior recordings to investigate the nematode C. elegans' response to time-varying mechanosensory signals. We use a high-throughput optogenetic assay, video microscopy and automated behavior quantification.

    453 views
  • Neuroscience
  • Last Updated On: 
    Tue, 11/12/2019 - 10:38