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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: -
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 -
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 -
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: Please download this dataset from DU Box as follows: https://durhamuniversity.box.com/s/bxp9bvfcz3anb3lk2f4f3z3rtqyug5b1
Note: Login is *not* required.Keywords: Fire detection, Deep learning, Machine learning, Computer vision Date Uploaded: 30 July 2018 -
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 -
Real-time traversable surface detection by colour space fusion and temporal analysis - Evaluation Dataset [Download]
Title: Real-time traversable surface detection by colour space fusion and temporal analysis - Evaluation Dataset Contributors: Contact person: Breckon, Toby P. (Durham University, UK)
Creator: Breckon, Toby P. (Durham University, UK)
Creator: Katramados, Ioannis (Cranfield University, UK)
Description: Data set from research paper: Real-Time Traversable Surface Detection by Colour Space Fusion and Temporal Analysis (I. Katramados, S. Crumpler, T.P. Breckon), In Proc. International Conference on Computer Vision Systems, Springer, Volume 5815, pp. 265-274, 2009. Previously released (2009-2015) as zip file download from personal web page of the authors (Katramados / Breckon). Keywords: computer vision, image processing, image analysis, robotics, path detection, robot guidance, traversable pathway, terrain classification, road following, robotic navigation Date Uploaded: 3 December 2015 -
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 -
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 -
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 -
Temporally Consistent Depth Enabled by a Multi-Task Approach [Neural Network Checkpoint]
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: