Occlusion edge detection requires both accurate locations and context constraints of the contour. Existing CNN-based pipeline does not utilize adaptive methods to filter the noise introduced by low-level features. To address this dilemma, we propose a novel Context-constrained accurate Contour Extraction Network (CCENet). Spatial details are retained and contour-sensitive context is augmented through two extraction blocks, respectively. Then, an elaborately designed fusion module is available to integrate features, which plays a complementary role to restore details and remove clutter. Weight response of attention mechanism is eventually utilized to enhance occluded contours and suppress noise. The proposed CCENet significantly surpasses state-of-the-art methods on PIOD and BSDS ownership dataset of object edge detection and occlusion orientation detection.
@article{arxiv.1903.08890,
title = {Context-Constrained Accurate Contour Extraction for Occlusion Edge Detection},
author = {Rui Lu and Menghan Zhou and Anlong Ming and Yu Zhou},
journal= {arXiv preprint arXiv:1903.08890},
year = {2019}
}