SkyFinder: attribute-based sky image search
ACM SIGGRAPH 2009 papers
Proceedings of the ACM International Conference on Image and Video Retrieval
The global network of outdoor webcams: properties and applications
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Finding Images with Similar Lighting Conditions in Large Photo Collections
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
What Do the Sun and the Sky Tell Us About the Camera?
International Journal of Computer Vision
Webcams in context: web interfaces to create live 3D environments
Proceedings of the international conference on Multimedia
Multimodal location estimation
Proceedings of the international conference on Multimedia
Determining the Geographical Location of Image Scenes based on Object Shadow Lengths
Journal of Signal Processing Systems
Multimodal location estimation on Flickr videos
WSM '11 Proceedings of the 3rd ACM SIGMM international workshop on Social media
Find you wherever you are: geographic location and environment context-based pedestrian detection
Proceedings of the ACM multimedia 2012 workshop on Geotagging and its applications in multimedia
Web-accessible geographic integration and calibration of webcams
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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As the main observed illuminant outdoors, the sky is a rich source of information about the scene. However, it is yet to be fully explored in computer vision because its appearance depends on the sun position, weather conditions, photometric and geometric parameters of the camera, and the location of capture. In this paper, we propose the use of a physically-based sky model to analyze the information available within the visible portion of the sky, observed over time. By fitting this model to an image sequence, we show how to extract camera parameters such as the focal length, and the zenith and azimuth angles. In short, the sky serves as a geometric calibration target. Once the camera parameters are recovered, we show how to use the same model in two applications: 1) segmentation of the sky and cloud layers, and 2) data-driven sky matching across different image sequences based on a novel similarity measure defined on sky parameters. This measure, combined with a rich appearance database, allows us to model a wide range of sky conditions.