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  1. On The Impact Of Varying Region Proposal Strategies For Raindrop Detection And Classification Using Convolutional Neural Networks - Supporting Materials

    Title: On The Impact Of Varying Region Proposal Strategies For Raindrop Detection And Classification Using Convolutional Neural Networks - Supporting Materials
    Contributors: Data collector: Guo, Tiancheng (Durham Univesity, UK)
    Data curator: Guo, Tiancheng (Durham Univesity, UK)
    Creator: Breckon, Toby (Durham University, UK)
    Data collector: Breckon, Toby (Durham University, UK)
    Data curator: Breckon, Toby (Durham University, UK)
    Editor: Breckon, Toby (Durham University, UK)
    Description:
    Keywords: Computer vision, Convolutional neural networks (CNN), Raindrop detection, Rain detection, Rain removal, Rain noise removal, Rain interference, Scene context, Raindrop saliency, Rain classification, Deep learning
    Date Uploaded:
  2. Pretrained Neural Network Models for Guo 2018 study - TensorFlow format [Download]

    Title: Pretrained Neural Network Models for Guo 2018 study - TensorFlow format
    Contributors: Creator: Guo, Tiancheng (Durham University, UK)
    Data collector: Guo, Tiancheng (Durham University, UK)
    Data curator: Guo, Tiancheng (Durham University, UK)
    Contact person: Breckon, Toby (Durham University, UK)
    Data curator: Breckon, Toby (Durham University, UK)
    Editor: Breckon, Toby (Durham University, UK)
    Description:
    Keywords: Computer vision, Raindrop detection, Rain detection, Rain removal, Rain noise removal, Rain interference, Scene context, Raindrop saliency, Rain classification, Convolutional neural network (CNN), Deep learning
    Date Uploaded: 25 November 2018
  3. Rain Drop Image Data Set for Guo 2018 study - still image set [Download]

    Title: Rain Drop Image Data Set for Guo 2018 study - still image set
    Contributors: Creator: Guo, Tiancheng (Durham University, UK)
    Data collector: Guo, Tiancheng (Durham University, UK)
    Data curator: Guo, Tiancheng (Durham University, UK)
    Contact person: Breckon, Toby (Durham University, UK)
    Data curator: Breckon, Toby (Durham University, UK)
    Editor: Breckon, Toby (Durham University, UK)
    Description:
    Keywords: Computer vision, Raindrop detection, Rain detection, Rain removal, Rain noise removal, Rain interference, Scene context, Raindrop saliency, Rain classification, Convolutional neural network (CNN), Deep learning
    Date Uploaded: 25 November 2018
  4. Fire Image Data Set for Dunnings 2018 study - PNG still image set [Download]

    Title: Fire Image Data Set for Dunnings 2018 study - PNG still image set
    Contributors: Creator: Dunnings, Andy (Durham University, UK)
    Data collector: Dunnings, Andy (Durham University, UK)
    Data curator: Dunnings, Andy (Durham University, UK)
    Contact person: Breckon, Toby (Durham University, UK)
    Editor: Breckon, Toby (Durham University, UK)
    Description:
    Keywords: Fire detection, Deep learning, Machine learning, Computer vision
    Date Uploaded: 30 July 2018
  5. Pretrained Neural Network Models for Dunnings 2018 study - TensorFlow format [Download]

    Title: Pretrained Neural Network Models for Dunnings 2018 study - TensorFlow format
    Contributors: Creator: Dunnings, Andy (Durham University, UK)
    Editor: Dunnings, Andy (Durham University, UK)
    Contact person: Breckon, Toby (Durham University, UK)
    Editor: Breckon, Toby (Durham University, UK)
    Description:
    Keywords: Convolutional Neural Network
    Date Uploaded: 26 July 2018
  6. Experimentally Defined Convolutional Neural Network Architecture Variants for Non-temporal Real-time Fire Detection - supporting materials

    Title: Experimentally Defined Convolutional Neural Network Architecture Variants for Non-temporal Real-time Fire Detection - supporting materials
    Contributors: Creator: Breckon, Toby (Durham University, UK)
    Editor: Breckon, Toby (Durham University, UK)
    Data curator: Dunnings, Andy (Durham University, UK)
    Data curator: Deshmukh, Atharva (Durham University, UK)
    Description:
    Keywords: fire detection, deep learning, machine learning , convolution neural network, computer vision, video analysis
    Date Uploaded: