  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.
