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  1. 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

  2. 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

  3. 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

  4. Improved 3D Sparse Maps for High-performance Structure from Motion with Low-cost Omnidirectional Robots - Evaluation Dataset [Download]

    Title: Improved 3D Sparse Maps for High-performance Structure from Motion with Low-cost Omnidirectional Robots - Evaluation Dataset
    Contributors: Creator: Breckon, Toby P. (Durham University, UK)
    Creator: Cavestany, Pedro (Durham University, UK)
    Contact person: Breckon, Toby P. (Durham University, UK)
    Description: Data set used in research paper doi:10.1109/ICIP.2015.7351744
    Keywords: Structure from motion, Mobile robot, Omnidirectional, Noise, Feature filtering
    Date Uploaded: 21 January 2016

  5. Neural network checkpoints [Download]

    Title: Neural network checkpoints
    Contributors: Creator: Atapour-Abarghouei, Amir (Durham University, UK)
    Creator: Breckon, Toby P. (Durham University, UK)
    Description: Neural network checkpoints associated with the work entitled: Real-time monocular depth estimation using synthetic data with domain adaptation via image style transfer / written by A. Atapour-Abarghouei and T.P. Breckon. -- In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2018 : Salt Lake City, Utah).
    Keywords: Computer vision, Image processing, Monocular depth estimation, Image style transfer, Domain adaptation
    Date Uploaded: 24 May 2018

  6. MD5 file checksums for Dur360BEV research data collection 2024-03-12 [Download]

    Title: MD5 file checksums for Dur360BEV research data collection 2024-03-12
    Contributors: Creator: Wenke, E (Durham University, UK)
    Editor: Wenke, E (Durham University, UK)
    Creator: Breckon, Toby P. (Durham University, UK)
    Editor: Breckon, Toby P. (Durham University, UK)
    Description:
    Keywords:
    Date Uploaded: 11 March 2025

  7. Pretrained Neural Network Models for Thomson 2020 study - PyTorch format [Download]

    Title: Pretrained Neural Network Models for Thomson 2020 study - PyTorch format
    Contributors: Creator: Thomson, William (Durham University, UK)
    Contact person: Bhowmik, Neelanjan (Durham University, UK)
    Editor: Bhowmik, Neelanjan (Durham University, UK)
    Editor: Breckon, Toby P. (Durham University, UK)
    Description:
    Keywords: Convolutional Neural Network , fire detection, classification
    Date Uploaded: 18 January 2021

  8. Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection - supporting materials

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    Title: Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection - supporting materials
    Contributors: Thomson, William (Durham University, UK)
    Bhowmik, Neelanjan (Durham University, UK)
    Breckon, Toby P. (Durham University, UK)
    Description:
    Keywords: Convolutional Neural Network, fire detection, classification, deep learning, machine learning, artificial intelligence
    Date Uploaded:

  9. Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach [Neural Network Weights] [Download]

    Title: Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach [Neural Network Weights]
    Contributors: Creator: Atapour-Abarghouei, Amir (Durham University, UK)
    Contact person: Atapour-Abarghouei, Amir (Durham University, UK)
    Editor: Atapour-Abarghouei, Amir (Durham University, UK)
    Contact person: Breckon, Toby P. (Durham University, UK)
    Editor: Breckon, Toby P. (Durham University, UK)
    Description: The data file has been created using PyTorch ( https://pytorch.org
    ) and contains the weights of a generative model trained to perform monocular depth estimation and semantic segmentation. The file is needed as part of the pipeline for the project at  https://github.com/atapour/temporal-depth-segmentation
     Instructions on how the file can be used and how the code is run can also be found at  https://github.com/atapour/temporal-depth-segmentation
    Keywords: Monocular Depth Estimation, Semantic Segmentation, Temporal Consistency, Computer Vision
    Date Uploaded: 15 March 2019

  10. Temporally Consistent Depth Enabled by a Multi-Task Approach [Neural Network Checkpoint]

    Collection icon
    Title: Temporally Consistent Depth Enabled by a Multi-Task Approach [Neural Network Checkpoint]
    Contributors: Creator: Amir Atapour-Abarghouei (Durham University, UK)
    Contact person: Amir Atapour-Abarghouei (Durham University, UK)
    Editor: Amir Atapour-Abarghouei (Durham University, UK)
    Contact person: Toby P. Breckon (Durham University, UK)
    Editor: Toby P. Breckon (Durham University, UK)
    Description:
    Keywords: Semantic segmentation, Temporal consistency, Monocular depth estimation, Scene understanding, Computer vision
    Date Uploaded: