Multimodal Vision Research Laboratory

MVRL

research area: image localization

The goal of image localization is to estimate the geographic location (or scene-relative location) of an image (or video). We worked on this problem for over 15 years, from a variety of perspectives. Our early work focused on localizing static outdoor cameras using temporal variations as a cue. More recent work has focused on the use of deep neural networks for single image localization, with a significant focus on exploiting overhead imagery for ground-level image localization.

See below for a list of our publications in this area. You can see an unfiltered list of our publications or lists filtered for the following research areas: agriculture; astronomical imagery and data; camera calibration; biodiversity and conservation; generative modeling; LiDAR Processing; image localization; medical and biological imaging; image motion; remote sensing and mapping; social media; video surveillance and object tracking; timelapse imaging; transportation; and outdoor webcam imagery.

publications

  1. a thumbnail for GOMAA-Geo: GOal Modality Agnostic Active Geo-localization
    Sarkar A, Sastry S, Pirinen A, Zhang C, Jacobs N, Vorobeychik Y. 2024. GOMAA-Geo: GOal Modality Agnostic Active Geo-localization. In: Neural Information Processing Systems (NeurIPS).
    bibtex | paper | linkedin | code
  2. Shugaev M, Semenov I, Ashley K, Klaczynski M, Cuntoor N, Lee MW, Jacobs N. 2024. ArcGeo: Localizing Limited Field-of-View Images using Cross-view Matching. In: IEEE Winter Conference on Applications of Computer Vision (WACV).
    bibtex | paper
  3. Xiong Z, Qiao F, Zhang Y, Jacobs N. 2023. StereoFlowGAN: Co-training for Stereo and Flow with Unsupervised Domain Adaptation. In: British Machine Vision Conference (BMVC).
    bibtex | paper | press release | linkedin
  4. a thumbnail for Content-Based Detection of Temporal Metadata Manipulation
    Padilha R, Salem T, Workman S, Andaló FA, Rocha A, Jacobs N. 2022. Content-Based Detection of Temporal Metadata Manipulation. IEEE Transactions on Information Forensics and Security:1316–1327. DOI: 10.1109/TIFS.2022.3159154.
    bibtex | paper | website | doi | tweet | code
  5. a thumbnail for A Structure-Aware Method for Direct Pose Estimation
    Blanton H, Workman S, Jacobs N. 2022. A Structure-Aware Method for Direct Pose Estimation. In: IEEE Winter Conference on Applications of Computer Vision (WACV). DOI: 10.1109/WACV51458.2022.00028.
    bibtex | paper | doi
  6. Blanton H, Greenwell C, Workman S, Jacobs N. 2020. Extending Absolute Pose Regression to Multiple Scenes. In: Joint Workshop on Long-Term Visual Localization, Visual Odometry and Geometric and Learning-based SLAM (CVPR Workshop). DOI: 10.1109/CVPRW50498.2020.00027.
    bibtex | paper | doi | tweet
  7. a thumbnail for Learning a Dynamic Map of Visual Appearance
    Salem T, Workman S, Jacobs N. 2020. Learning a Dynamic Map of Visual Appearance. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). DOI: 10.1109/CVPR42600.2020.01245.
    bibtex | paper | website | doi | tweet
  8. a thumbnail for Learning Geo-Temporal Image Features
    Zhai M, Salem T, Greenwell C, Workman S, Pless R, Jacobs N. 2018. Learning Geo-Temporal Image Features. In: British Machine Vision Conference (BMVC).
    bibtex | paper
  9. a thumbnail for Predicting Ground-Level Scene Layout from Aerial Imagery
    Zhai M, Bessinger Z, Workman S, Jacobs N. 2017. Predicting Ground-Level Scene Layout from Aerial Imagery. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). DOI: 10.1109/CVPR.2017.440.
    bibtex | paper | doi | tweet | code
  10. Vo N, Jacobs N, Hays J. 2017. Revisiting IM2GPS in the Deep Learning Era. In: IEEE International Conference on Computer Vision (ICCV). DOI: 10.1109/ICCV.2017.286.
    bibtex | paper | website | doi
  11. a thumbnail for Horizon Lines in the Wild
    Workman S, Zhai M, Jacobs N. 2016. Horizon Lines in the Wild. In: British Machine Vision Conference (BMVC).
    bibtex | paper | website | code
  12. Zhai M, Workman S, Jacobs N. 2016. Camera Geo-Calibration using an MCMC Approach. In: IEEE International Conference on Image Processing (ICIP). DOI: 10.1109/ICIP.2016.7532905.
    bibtex | paper | doi
  13. Baltenberger R, Zhai M, Greenwell C, Workman S, Jacobs N. 2016. A Fast Method for Estimating Transient Scene Properties. In: IEEE Winter Conference on Applications of Computer Vision (WACV). 1–8. DOI: 10.1109/WACV.2016.7477713.
    bibtex | paper | website | doi | code
  14. Islam MT, Workman S, Jacobs N. 2015. Face2GPS: Estimating Geographic Location from Facial Features. In: IEEE International Conference on Image Processing (ICIP). DOI: 10.1109/ICIP.2015.7351072.
    bibtex | paper | website | doi
  15. a thumbnail for Wide-Area Image Geolocalization with Aerial Reference Imagery
    Workman S, Souvenir R, Jacobs N. 2015. Wide-Area Image Geolocalization with Aerial Reference Imagery. In: IEEE International Conference on Computer Vision (ICCV). 1–9. DOI: 10.1109/ICCV.2015.451.
    bibtex | paper | website | doi | code
  16. Workman S, Jacobs N. 2015. On the Location Dependence of Convolutional Neural Network Features. In: IEEE/ISPRS Workshop: Large Scale Computer Vision for Remote Sensing (EARTHVISION). 1–9. DOI: 10.1109/CVPRW.2015.7301385.
    bibtex | paper | doi
  17. Jacobs N, Miskell K, Pless R. 2011. Webcam Geo-localization using Aggregate Light Levels. In: IEEE Workshop on Applications of Computer Vision (WACV). 132–138. DOI: 10.1109/WACV.2011.5711494.
    bibtex | paper | website | doi
  18. Jacobs N, Roman N, Pless R. 2008. Toward Fully Automatic Geo-Location and Geo-Orientation of Static Outdoor Cameras. In: IEEE Workshop on Applications of Computer Vision (WACV). 1–6. DOI: 10.1109/WACV.2008.4544040.
    bibtex | paper | doi
  19. a thumbnail for Geolocating Static Cameras
    Jacobs N, Satkin S, Roman N, Speyer R, Pless R. 2007. Geolocating Static Cameras. In: IEEE International Conference on Computer Vision (ICCV). 1–6. DOI: 10.1109/ICCV.2007.4408995.
    bibtex | paper | website | doi