Bio: Jean-Francois Lalonde received his B.E. in Computer Engineering from
Laval University, Canada in 2004. He received his M.S. in Robotics from
Carnegie Mellon University in 2006 under Martial Hebert, and he has been
a Robotics Ph.D. student advised by Alexei A. Efros in that institution
since. His research interests are in computer vision and computer
graphics, focusing on image understanding and synthesis
leveraging large amounts of data.

Abstract: 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 talk, I will present an
analysis of 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.