Deepstrip: High-Resolution Boundary Refinement

By Peng Zhou, Brian L. Price, Scott D. Cohen, Gregg Wilensky, Larry S. Davis. 2020

25 citations recorded by the source corpus.

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 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.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Reviving Iterative Training with Mask Guidance for Interactive Segmentation (2021, methodology): “These methods can either be a part of the network architecture [28, 29] or serve as a postprocessing step [30, 31].”