posted on 2008-01-01, 00:00authored byJean-Francois Lalonde, Srinivasa G. Narasimhan, Alexei A Efros
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 ftting 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.