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Probability of shadow
Detecting shadows from images can significantly improve the
performance of several vision tasks such as object detection and
tracking. Recent approaches have mainly used illumination invariants
which can fail severely when the qualities of the images are not
very good, as is the case for most consumer-grade photographs, like
those on Google or Flickr. We present a practical algorithm to
automatically detect shadows cast by objects onto the ground, from a
single consumer photograph. Our key hypothesis is that the types of
materials constituting the ground in outdoor scenes is relatively
limited, most commonly including asphalt, brick, stone, mud, grass,
concrete, etc. As a result, the appearances of shadows on the ground
are not as widely varying as general shadows and thus, can be
learned from a labelled set of images. Our detector consists of a
three-tier process including (a) training a decision tree classifier
on a set of shadow sensitive features computed around each image
edge, (b) a CRF-based optimization to group detected shadow edges to
generate coherent shadow contours, and (c) incorporating any
existing classifier that is specifically trained to detect grounds
in images. Our results demonstrate good detection accuracy (85%) on
several challenging images. Since most objects of interest to vision
applications (like pedestrians, vehicles, signs) are attached to the
ground, we believe that our detector can find wide applicability.
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