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Sky/cloud segmentation
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 in an image
depends on the sun position, weather conditions, photometric and
geometric parameters of the camera, and the location of capture. In
this paper, we analyze two sources of information available within
the visible portion of the sky region: the sun position, and
the sky appearance. By fitting a model of the predicted sun
position to an image sequence, we show how to extract camera
parameters such as the focal length, and the zenith and azimuth
angles. Similarly, we show how we can extract the same parameters by
fitting a physically-based sky model to the sky appearance. In
short, the sun and the sky serve as geometric calibration targets,
which can be used to annotate a large database of image sequences.
We use our methods to calibrate 22 real, low-quality webcam
sequences scattered throughout the continental US, and show
deviations below 4% for focal length, and 3 degrees for the
zenith and azimuth angles. Once the camera parameters are recovered,
we use them to define a camera-invariant sky appearance model, which
we exploit 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.
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