This paper proposes a Deep Learning based edge detector, which is inspired on both HED
(Holistically-Nested Edge Detection) and Xception networks. The proposed approach …

Edge detection is the basis of many computer vision applications. State of the art
predominantly relies on deep learning with two decisive factors: dataset content and network …

In this paper, we propose an accurate edge detector using richer convolutional features
(RCF). Since objects in natural images possess various scales and aspect ratios, learning …

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 …

Abstract Recently, deep Convolutional Neural Networks (CNNs) can achieve human-level
performance in edge detection with the rich and abstract edge representation capacities …

Exploiting multi-scale representations is critical to improve edge detection for objects at
different scales. To extract edges at dramatically different scales, we propose a bi-directional …

Exploiting multi-scale representations is critical to improve edge detection for objects at
different scales. To extract edges at dramatically different scales, we propose a Bi …

Edge detection is represented as one of the most challenging tasks in computer vision, due
to the complexity of detecting the edges or boundaries in real-world images that contains …

Convolutional neural networks have made significant progresses in edge detection by
progressively exploring the context and semantic features. However, local details are …

We propose using relaxed deep supervision (RDS) within convolutional neural networks for
edge detection. The conventional deep supervision utilizes the general ground-truth to …