Abstract
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.
Abstract (translated)
遮挡边缘检测需要轮廓的精确位置和上下文约束。现有的基于CNN的管道不采用自适应方法来滤除由低电平特性引入的噪声。为了解决这一难题，我们提出了一种新的上下文约束的精确轮廓提取网络（ccenet）。保留空间细节，分别通过两个提取块增强轮廓敏感上下文。然后，一个精心设计的融合模块可以集成功能，这对恢复细节和消除混乱起到了补充作用。最后利用注意机制的权值响应增强遮挡轮廓，抑制噪声。所提出的ccenet在对象边缘检测和遮挡方向检测的PIOD和BSDS所有权数据集上明显优于最先进的方法。
URL
https://arxiv.org/abs/1903.08890
