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Abstract

We address the problem of reconstructing the background of
a scene from a set of photographs featuring several occluding
objects. We assume that the photographs are obtained from the same
viewpoint and under similar illumination conditions. Our approach
is to define the background as a composite of the input photographs.
Each possible composite is assigned a cost, and the resulting cost
function is minimized. We penalize deviations from the following
two model assumptions: background objects are stationary, and
background objects are more likely to appear across the photographs.
We approximate object stationariness using a motion boundary
consistency term, and object likelihood using probability density
estimates. The penalties are combined using an entropy-based
weighting function. Furthermore, we constraint the solution space
in order to avoid composites that cut through objects. The cost
function is minimized using graph cuts, and the final result is
composed using gradient domain fusion.
We demonstrate the application of our method to the recovering of
clean, unoccluded shots of crowded public places, as well as to the
removal of ghosting artifacts in the reconstruction of high dynamic
range images from multi-exposure sequences. Our contribution is the
definition of an automatic method for consistent background
estimation from multiple exposures featuring occluders, and its
application to the problem of ghost removal in high dynamic range
image reconstruction.