Edge Detection
118 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 )
Benchmarks
These leaderboards are used to track progress in Edge Detection
Libraries
Use these libraries to find Edge Detection models and implementations
Cityscapes
BSDS500
SBD
BIPED
MDBD
UDED
INRIA-Horse
Most implemented papers
Holistically-Nested Edge Detection
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
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
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NeurIPS 2018
We find that reliance, solely, on visual assessment can be misleading.
HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline
khdlr/HED-UNet
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Deep learning-based coastline detection algorithms have begun to outshine traditional statistical methods in recent years.
Multimodal Generative Models for Scalable Weakly-Supervised Learning
mhw32/multimodal-vae-public
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NeurIPS 2018
Multiple modalities often co-occur when describing natural phenomena.
Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection
xavysp/DexiNed
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This dataset has been used for training the proposed approach as well the state-of-the-art algorithms for comparisons.
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
kornia/kornia
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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.
Fast Detection of Curved Edges at Low SNR
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
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
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CVPR 2017
Using VGG16 network, we achieve \sArt results on several available datasets.
