  In this paper, we target refining the boundaries in high resolution images
given low resolution masks. For memory and computation efficiency, we propose
to convert the regions of interest into strip images and compute a boundary
prediction in the strip domain. To detect the target boundary, we present a
framework with two prediction layers. First, all potential boundaries are
predicted as an initial prediction and then a selection layer is used to pick
the target boundary and smooth the result. To encourage accurate prediction, a
loss which measures the boundary distance in the strip domain is introduced. In
addition, we enforce a matching consistency and C0 continuity regularization to
the network to reduce false alarms. Extensive experiments on both public and a
newly created high resolution dataset strongly validate our approach.
