Image Processing

 

Dataset was created as part of joint efforts of two research groups from the University of Novi Sad, which were aimed towards development of vision based systems for automatic identification of insect species (in particular hoverflies) based on characteristic venation patterns in the images of the insects' wings.The set of wing images consists of high-resolution microscopic wing images of several hoverfly species. There is a total of 868 wing images of eleven selected hoverfly species from two different genera, Chrysotoxum and Melanostoma.

513 views
  • Computer Vision
  • Last Updated On: 
    Thu, 12/12/2019 - 13:38

    Optical sectioning microscopy is usually performed by means of a scanning, multi-shot procedure in combination with non-uniform illumination. In this paper we change the paradigm and report a method that is based in the lightfield concept, and that provides optical sectioning for 3D microscopy images after a single-shot capture. To do this we first capture multiple orthographic perspectives of the sample by means of Fourier-domain integral microscopy (FiMic).

    50 views
  • Image Processing
  • Last Updated On: 
    Wed, 10/23/2019 - 06:12

    This dataset was developed at the School of Electrical and Computer Engineering (ECE) at the Georgia Institute of Technology as part of the ongoing activities at the Center for Energy and Geo-Processing (CeGP) at Georgia Tech and KFUPM. LANDMASS stands for “LArge North-Sea Dataset of Migrated Aggregated Seismic Structures”. This dataset was extracted from the North Sea F3 block under the Creative Commons license (CC BY-SA 3.0).

    212 views
  • Artificial Intelligence
  • Last Updated On: 
    Mon, 10/21/2019 - 12:54

    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.

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

    The dataset contains high-resolution microscopy images and confocal spectra of semiconducting single-wall carbon nanotubes. Carbon nanotubes allow down-scaling of electronic components to the nano-scale. There is initial evidence from Monte Carlo simulations that microscopy images with high digital resolution show energy information in the Bessel wave pattern that is visible in these images. In this dataset, images from Silicon and InGaAs cameras, as well as spectra, give valuable insights into the spectroscopic properties of these single-photon emitters.

    364 views
  • Artificial Intelligence
  • Last Updated On: 
    Thu, 12/12/2019 - 13:43

    The dataset consists of 60285 character image files which has been randomly divided into 54239 (90%) images as training set 6046 (10%) images as test set. The collection of data samples was carried out in two phases. The first phase consists of distributing a tabular form and asking people to write the characters five times each. Filled-in forms were collected from around 200 different individuals in the age group 12-23 years. The second phase was the collection of handwritten sheets such as answer sheets and classroom notes from students in the same age group.

    245 views
  • Computer Vision
  • Last Updated On: 
    Mon, 10/28/2019 - 01:08

    As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (1.M images) object recognition dataset (CURE-OR) which is among the most comprehensive datasets with controlled synthetic challenging conditions. In CURE

    324 views
  • Artificial Intelligence
  • Last Updated On: 
    Sun, 10/13/2019 - 17:05

    As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (~1.72M frames) traffic sign detection video dataset (CURE-TSD) which is among the most comprehensive datasets with controlled synthetic challenging conditions. The video sequences in the 

    513 views
  • Artificial Intelligence
  • Last Updated On: 
    Sun, 10/13/2019 - 17:07

    This data set contains 50 low resolution (640 x 360) short videos containing a variety real life activities.

    81 views
  • Image Processing
  • Last Updated On: 
    Mon, 10/21/2019 - 09:56

    This Dataset consist of 3Dmodels in Spherical harmonic coefficients andcorresponding shortcut of front view, the SH degree is 80.

    24 views
  • Image Processing
  • Last Updated On: 
    Sat, 10/12/2019 - 22:55

    Pages