论文标题
使用逻辑改进网络学习清晰的边缘检测器
Learning Crisp Edge Detector Using Logical Refinement Network
论文作者
论文摘要
边缘检测是不同计算机视觉任务的基本问题。最近，边缘检测算法实现了基于深度学习的令人满意的改进。尽管他们中的大多数都报告了有利的评估得分，但它们通常无法准确地定位边缘并给出较厚且模糊的边界。此外，其中大多数集中在2D图像上，并且具有挑战性的3D边缘检测仍然不足。在这项工作中，我们提出了一个新型的逻辑完善网络，用于清晰的边缘检测，这是由分割和边缘图之间的逻辑关系激励的，并且可以应用于2D和3D图像。该网络由一个联合对象和边缘检测网络以及一个清晰的边缘改进网络组成，该网络可预测更准确，更清晰，更薄的高质量二进制边缘地图，而无需任何后处理。在Kaggle 2018 Data Science Bowl和猴子大脑的私有3D显微镜图像上对2D核图像进行了广泛的实验，与最先进的方法相比，它们的性能出色。
Edge detection is a fundamental problem in different computer vision tasks. Recently, edge detection algorithms achieve satisfying improvement built upon deep learning. Although most of them report favorable evaluation scores, they often fail to accurately localize edges and give thick and blurry boundaries. In addition, most of them focus on 2D images and the challenging 3D edge detection is still under-explored. In this work, we propose a novel logical refinement network for crisp edge detection, which is motivated by the logical relationship between segmentation and edge maps and can be applied to both 2D and 3D images. The network consists of a joint object and edge detection network and a crisp edge refinement network, which predicts more accurate, clearer and thinner high quality binary edge maps without any post-processing. Extensive experiments are conducted on the 2D nuclei images from Kaggle 2018 Data Science Bowl and a private 3D microscopy images of a monkey brain, which show outstanding performance compared with state-of-the-art methods.
