Dataset Description

A large dataset of images annotated with horizon lines.

Additional Resources

• inference example github repo
• inference using the public API on algorithmia
• If you want access to the HLW dataset, please submit a request.

Dataset Citation

If you use the dataset, please cite the following publications:

1. Workman S, Zhai M, Jacobs N. 2016. Horizon Lines in the Wild. In: British Machine Vision Conference (BMVC).
   bibtex | paper | website | code

   close  

   @inproceedings{workman2016hlw,
     annotation = {hlw,dataset_hlw,spotlight,waldo,nvidia,localization,calibration},
     author = {Workman, Scott and Zhai, Menghua and Jacobs, Nathan},
     booktitle = {British Machine Vision Conference (BMVC)},
     note = {Acceptance rate: 39.4\%},
     code = {https://github.com/scottworkman/deephorizon},
     pdf = {https://arxiv.org/pdf/1604.02129},
     project = {/datasets/hlw/},
     thumbnail = {/thumbnails/hlw.jpg},
     title = {Horizon Lines in the Wild},
     year = {2016}
   }
   

Related Publication(s)

1. Workman S, Zhai M, Jacobs N. 2016. Horizon Lines in the Wild. In: British Machine Vision Conference (BMVC).
   bibtex | paper | website | code

   close  

   @inproceedings{workman2016hlw,
     annotation = {hlw,dataset_hlw,spotlight,waldo,nvidia,localization,calibration},
     author = {Workman, Scott and Zhai, Menghua and Jacobs, Nathan},
     booktitle = {British Machine Vision Conference (BMVC)},
     note = {Acceptance rate: 39.4\%},
     code = {https://github.com/scottworkman/deephorizon},
     pdf = {https://arxiv.org/pdf/1604.02129},
     project = {/datasets/hlw/},
     thumbnail = {/thumbnails/hlw.jpg},
     title = {Horizon Lines in the Wild},
     year = {2016}
   }
   

2. Zhai M, Workman S, Jacobs N. 2016. Detecting Vanishing Points using Global Image Context in a Non-Manhattan World. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). DOI: 10.1109/CVPR.2016.610.
   bibtex | paper | doi | code

   close  

   @inproceedings{zhai2016horizon,
     annotation = {spotlight,hlw,calibration,deeplearning,nvidia,contextualeyes},
     author = {Zhai, Menghua and Workman, Scott and Jacobs, Nathan},
     booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
     code = {https://github.com/viibridges/gc-horizon-detector},
     pdf = {http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhai_Detecting_Vanishing_Points_CVPR_2016_paper.pdf},
     thumbnail = {/thumbnails/horizon_sampling.jpg},
     title = {Detecting Vanishing Points using Global Image Context in a Non-{Manhattan} World},
     note = {Acceptance rate: 29.9\%},
     doi = {10.1109/CVPR.2016.610},
     year = {2016}
   }
   

3. Workman S, Greenwell C, Zhai M, Baltenberger R, Jacobs N. 2015. DeepFocal: A Method for Direct Focal Length Estimation. In: IEEE International Conference on Image Processing (ICIP). DOI: 10.1109/ICIP.2015.7351024.
   bibtex | paper | website | doi

   close  

   @inproceedings{workman2015deepfocal,
     annotation = {calibration,deeplearning,contextualeyes,hlw,calibration,social_media},
     author = {Workman, Scott and Greenwell, Connor and Zhai, Menghua and Baltenberger, Ryan and Jacobs, Nathan},
     booktitle = {IEEE International Conference on Image Processing (ICIP)},
     note = {Acceptance rate: 45\% (overall)},
     project = {http://cs.uky.edu/%7escott/research/deepfocal/},
     doi = {10.1109/ICIP.2015.7351024},
     pdf = {https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=bfb395784e2fe214164e857e14db4557acdbf94b},
     title = {{DeepFocal}: A Method for Direct Focal Length Estimation},
     year = {2015}
   }