DeepStrip: High Resolution Boundary Refinement

TL;DR

DeepStrip introduces a boundary refinement method for high resolution images using strip domain predictions, a two-stage boundary detection framework, and regularization techniques, validated by extensive experiments.

Contribution

It proposes a novel strip domain approach for boundary refinement in high resolution images, improving accuracy and efficiency over existing methods.

Findings

Effective boundary refinement demonstrated on public datasets.

Significant reduction in false alarms and improved boundary accuracy.

Validated on a new high resolution dataset.

Abstract

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.