计算机科学
水准点(测量)
GSM演进的增强数据速率
刮擦
人工智能
模式识别(心理学)
建筑
边缘检测
网络体系结构
机器学习
图像(数学)
图像处理
艺术
视觉艺术
操作系统
计算机安全
地理
大地测量学
作者
Xavier Soria Poma,Ángel D. Sappa,Patricio Humanante,Arash Akbarinia
标识
DOI:10.1016/j.patcog.2023.109461
摘要
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 architecture. Most of the publicly available datasets are not curated for edge detection tasks. Here, we address this limitation. First, we argue that edges, contours and boundaries, despite their overlaps, are three distinct visual features requiring separate benchmark datasets. To this end, we present a new dataset of edges. Second, we propose a novel architecture, termed Dense Extreme Inception Network for Edge Detection (DexiNed), that can be trained from scratch without any pre-trained weights. DexiNed outperforms other algorithms in the presented dataset. It also generalizes well to other datasets without any fine-tuning. The higher quality of DexiNed is also perceptually evident thanks to the sharper and finer edges it outputs.
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