计算机科学
人工智能
卷积神经网络
GSM演进的增强数据速率
边缘检测
模棱两可
图像(数学)
深度学习
目标检测
特征(语言学)
模式识别(心理学)
像素
视觉对象识别的认知神经科学
特征提取
人工神经网络
计算机视觉
图像处理
程序设计语言
哲学
语言学
标识
DOI:10.1109/iccv.2015.164
摘要
We develop a new edge detection algorithm that addresses two critical issues in this long-standing vision problem: (1) holistic image training, and (2) multi-scale feature learning. Our proposed method, holistically-nested edge detection (HED), turns pixel-wise edge classification into image-to-image prediction by means of a deep learning model that leverages fully convolutional neural networks and deeply-supervised nets. HED automatically learns rich hierarchical representations (guided by deep supervision on side responses) that are crucially important in order to approach the human ability to resolve the challenging ambiguity in edge and object boundary detection. We significantly advance the state-of-the-art on the BSD500 dataset (ODS F-score of 0.782) and the NYU Depth dataset (ODS F-score of 0.746), and do so with an improved speed (0.4 second per image) that is orders of magnitude faster than recent CNN-based edge detection algorithms.
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