Image Processing

This dataset extends the Urban Semantic 3D (US3D) dataset developed and first released for the 2019 IEEE GRSS Data Fusion Contest (DFC19). We provide additional geographic tiles to supplement the DFC19 training data and also new data for each tile to enable training and validation of models to predict geocentric pose, defined as an object's height above ground and orientation with respect to gravity. We also add to the DFC19 data from Jacksonville, Florida and Omaha, Nebraska with new geographic tiles from Atlanta, Georgia.

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  • Computer Vision
  • Last Updated On: 
    Sat, 06/27/2020 - 10:26

    In this the  Indian Cautionary Traffic sign data-set has been proposed for classifying the cautionary traffic signs. It is composed of more than 9900 images clustered in 17 different classes. The dataset is trained with different convolutional neural networks and the performance of the classification has been compared and analyzed and achieved high performance when compared with state - of- the - art methodologies

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  • Computer Vision
  • Last Updated On: 
    Thu, 06/18/2020 - 08:04

    Supplementary material for the paper: 'Reconstruct Clear Image for High-Speed Motion Scene with Retina-Inspired Spike Camera'

     

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  • Image Processing
  • Last Updated On: 
    Sun, 06/07/2020 - 01:29

    Synthetic Aperture Radar (SAR) images can be extensively informative owing to their resolution and availability. However, the removal of speckle-noise from these requires several pre-processing steps. In recent years, deep learning-based techniques have brought significant improvement in the domain of denoising and image restoration. However, further research has been hampered by the lack of availability of data suitable for training deep neural network-based systems. With this paper, we propose a standard synthetic data set for the training of speckle reduction algorithms.

    194 views
  • Computer Vision
  • Last Updated On: 
    Fri, 06/12/2020 - 08:50

    The dataset consists of subjective evaluations of 44 naive observers judging the visual complexity of 16 images. The subjective judgments were done using a 5-point Likert-type scale with a neutral midpoint. The items in the scale were “very complex,” “complex,” “medium,” “simple,” and “very simple.” The order of the images was randomized for every participant.

    69 views
  • Image Processing
  • Last Updated On: 
    Tue, 05/26/2020 - 09:34

    This is a collection of paired thermal and visible ear images. Images in this dataset were acquired in different illumination conditions ranging between 2 and 10700 lux. There are total 2200 images of which 1100 are thermal images while the other 1100 are their corresponding visible images. Images consisted of left and right ear images of 55 subjects. Images were capture in 5 illumination conditiond for every subjects. This dataset was developed for illumination invariant ear recognition study. In addition it can also be useful for thermal and visible image fusion research.

     

    55 views
  • Image Processing
  • Last Updated On: 
    Sat, 05/23/2020 - 05:49

    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. 

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

    As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on recognizing textures and materials in real-world images, which plays an important role in object recognition and scene understanding. Aiming at describing objects or scenes with more detailed information, we explore how to computationally characterize apparent or latent properties (e.g. surface smoothness) of materials, i.e., computational material characterization, which moves a step further beyond material recognition.

    114 views
  • Artificial Intelligence
  • Last Updated On: 
    Wed, 05/20/2020 - 01:38

    As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on recognizing textures and materials in real-world images, which plays an important role in object recognition and scene understanding. Aiming at describing objects or scenes with more detailed information, we explore how to computationally characterize apparent or latent properties (e.g. surface smoothness) of materials, i.e., computational material characterization, which moves a step further beyond material recognition.

    47 views
  • Artificial Intelligence
  • Last Updated On: 
    Wed, 05/20/2020 - 00:25

    The Costas condition on a permutation matrix, expressed as row indices as elements of a vector c, can be expressed as A*c=b, where b is a vector of integers in which no element is zero.  A particular formulation of the matrix A allows a singular value decomposition in which the eigenvalues are squared integers and the eigenvalues may be scaled to vectors with all integer elements.  This is a database of the Costas constraint matrices A, the scaled eigenvectors, and the squared eigenvalues for orders 3 through 100.

    142 views
  • Image Processing
  • Last Updated On: 
    Tue, 06/09/2020 - 04:30

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