Edge Detection

123 papers with code • 8 benchmarks • 9 datasets

Edge Detection is a fundamental image processing technique which involves computing an image gradient to quantify the magnitude and direction of edges in an image. Image gradients are used in various downstream tasks in computer vision such as line detection, feature detection, and image classification.

Source: Artistic Enhancement and Style Transfer of Image Edges using Directional Pseudo-coloring

( Image credit: Kornia )


Use these libraries to find Edge Detection models and implementations
2 papers

Most implemented papers

Holistically-Nested Edge Detection

s9xie/hed ICCV 2015

We develop a new edge detection algorithm that tackles two important issues in this long-standing vision problem: (1) holistic image training and prediction; and (2) multi-scale and multi-level feature learning.

CASENet: Deep Category-Aware Semantic Edge Detection

Lavender105/DFF CVPR 2017

To this end, we propose a novel end-to-end deep semantic edge learning architecture based on ResNet and a new skip-layer architecture where category-wise edge activations at the top convolution layer share and are fused with the same set of bottom layer features.

Sanity Checks for Saliency Maps

adebayoj/sanity_checks_saliency NeurIPS 2018

We find that reliance, solely, on visual assessment can be misleading.

Kornia: an Open Source Differentiable Computer Vision Library for PyTorch

kornia/kornia 5 Oct 2019

This work presents Kornia -- an open source computer vision library which consists of a set of differentiable routines and modules to solve generic computer vision problems.

HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline

khdlr/HED-UNet 2 Mar 2021

Deep learning-based coastline detection algorithms have begun to outshine traditional statistical methods in recent years.

Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection

xavysp/DexiNed 4 Sep 2019

This dataset has been used for training the proposed approach as well the state-of-the-art algorithms for comparisons.

Fast Detection of Curved Edges at Low SNR

NatiOfir/TrianglesEdgeDetection CVPR 2016

Detecting edges is a fundamental problem in computer vision with many applications, some involving very noisy images.

Object Contour Detection with a Fully Convolutional Encoder-Decoder Network

jimeiyang/objectContourDetector CVPR 2016

We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network.

Richer Convolutional Features for Edge Detection

yun-liu/rcf CVPR 2017

Using VGG16 network, we achieve \sArt results on several available datasets.