Event Location: NSH 1507

Abstract: Building on recent advances in the detection of appearance edges from
multiple local cues, we present an approach for detecting occlusion
boundaries which also incorporates local motion information. We argue
that these boundaries have physical significance which makes them
important for many high-level vision tasks and that motion offers a
unique, often critical source of additional information for detecting
them. We provide a new dataset of natural image sequences with labeled
occlusion boundaries, on which we learn a classifier that leverages
appearance cues along with motion estimates from either side of an edge.
We demonstrate improved performance for pixelwise differentiation of
occlusion boundaries from non-occluding edges by combining these weak
local cues, as compared to using them separately. The results are
suitable as improved input to subsequent mid- or high-level reasoning
methods.