Image Processing

This dataset contains the trained model that accompanies the publication of the same name:

 Anup Tuladhar*, Serena Schimert*, Deepthi Rajashekar, Helge C. Kniep, Jens Fiehler, Nils D. Forkert, "Automatic Segmentation of Stroke Lesions in Non-Contrast Computed Tomography Datasets With Convolutional Neural Networks," in IEEE Access, vol. 8, pp. 94871-94879, 2020, doi:10.1109/ACCESS.2020.2995632. *: Co-first authors

 

189 views
  • Artificial Intelligence
  • Last Updated On: 
    Wed, 06/10/2020 - 20:23

    Objective: No data currently exist on the reproducibility of photographic food records compared to diet diaries, two commonly used methods to measure habitual dietary intake. Our aim was to examine the reproducibility of diet diaries, photographic food records, and a novel electronic sensor, consisting of counts of chews and swallows using wearable sensors and video analysis, for estimating energy intake.

    46 views
  • Image Processing
  • Last Updated On: 
    Thu, 05/14/2020 - 12:01

    Supplementary material for the paper: 'Adaptive Block Compressive Imaging: towards a real-time and low complexity implementation'

    74 views
  • Image Processing
  • Last Updated On: 
    Tue, 06/16/2020 - 19:15

    The supplementary files of our submitted TIFS paper: "CALPA-NET: Channel-pruning-assisted Deep Residual Network for Steganalysis of Digital Images".

    27 views
  • Image Processing
  • Last Updated On: 
    Sat, 05/09/2020 - 05:13

    This aerial image dataset consists of more than 22,000 independent buildings extracted from aerial images with 0.0075 m spatial resolution and 450 km^2 covering in Christchurch, New Zealand. The most parts of aerial images are down-sampled to 0.3 m ground resolution and cropped into 8,189 non-overlapping tiles with 512* 512. These tiles make up the whole dataset. They are split into three parts: 4,736 tiles for training, 1,036 tiles for validation and 2,416 tiles for testing.

    26 views
  • Computer Vision
  • Last Updated On: 
    Fri, 05/08/2020 - 19:56

    This Dataset contains "Pristine" and "Distorted" videos recorded in different places. The 

    distortions with which the videos were recorded are: "Focus", "Exposure" and "Focus + Exposure". 

    Those three with low (1), medium (2) and high (3) levels, forming a total of 10 conditions 

    (including Pristine videos). In addition, distorted videos were exported in three different 

    qualities according to the H.264 compression format used in the DIGIFORT software, which were: 

    High Quality (HQ, H.264 at 100%), Medium Quality (MQ, H.264 at 75%) and Low Quality 

    135 views
  • Computer Vision
  • Last Updated On: 
    Thu, 05/07/2020 - 19:27

    Dataset asscociated with a paper to appear in IEEE Transactions on Pattern Analysis and Machine Intelligence

    "The perils and pitfalls of block design for EEG classification experiments"

    The paper has been accepted and is in production.

    We will upload the dataset when the paper is published.

    This is a placeholder so we can obtain a DOI to include in the paper.

    73 views
  • Artificial Intelligence
  • Last Updated On: 
    Fri, 04/24/2020 - 16:39

     

    38 views
  • Computer Vision
  • Last Updated On: 
    Mon, 05/18/2020 - 19:37

    [17-APR-2020: WE ARE STILL UPLOADING THE DATASET, PLEASE WAIT UNTIL IT IS COMPLETED] -The dataset comprises a set of 11 different actions performed by 17 subjects that is created for multimodal fall detection. Five types of falls and six daily activities were considered in the experiment. Data collection comes from five wearable sensors, one brainwave helmet sensor, six infrared sensors around the room and two RGB-cameras. Three attempts per action were recorded. The dataset contains raw signals as well as three windowing-based feature sets.

    142 views
  • Artificial Intelligence
  • Last Updated On: 
    Mon, 05/11/2020 - 20:12

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