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
MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentation
MEGANet is designed as an end-to-end framework, encompassing three key modules: an encoder, which is responsible for capturing and abstracting the features from the input image, a decoder, which focuses on salient features, and the Edge-Guided Attention module (EGA) that employs the Laplacian Operator to accentuate polyp boundaries.
Convolutional Channel Features
With the combination of CNN features and boosting forest, CCF benefits from the richer capacity in feature representation compared with channel features, as well as lower cost in computation and storage compared with end-to-end CNN methods.
On Image segmentation using Fractional Gradients-Learning Model Parameters using Approximate Marginal Inference
Estimates of image gradients play a ubiquitous role in image segmentation and classification problems since gradients directly relate to the boundaries or the edges of a scene.
Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection
deep-unlearn/ISPRS-Classification-With-an-Edge
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We present an end-to-end trainable deep convolutional neural network (DCNN) for semantic segmentation with built-in awareness of semantically meaningful boundaries.
PixelNet: Representation of the pixels, by the pixels, and for the pixels
bdecost/pixelnet
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We explore design principles for general pixel-level prediction problems, from low-level edge detection to mid-level surface normal estimation to high-level semantic segmentation.
Multi-scale Processing of Noisy Images using Edge Preservation Losses
As our experiments show, we achieve high-quality results in the three aspects of faint edge detection, noisy image classification and natural image denoising.
Semantic Edge Detection with Diverse Deep Supervision
arsenal9971/shearlet_semantic_edge
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Semantic edge detection (SED), which aims at jointly extracting edges as well as their category information, has far-reaching applications in domains such as semantic segmentation, object proposal generation, and object recognition.
Adaptive edge detection algorithm for multi-focus application
This paper proposes a method of solving the problem of multi-focus through edge detection based on adaptive thresholding.
SRN: Side-output Residual Network for Object Reflection Symmetry Detection and Beyond
KevinKecc/SRN
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The end-to-end deep learning approach, referred to as a side-output residual network (SRN), leverages the output residual units (RUs) to fit the errors between the object ground-truth symmetry and the side-outputs of multiple stages.
Learning to predict crisp boundaries
yunfan1202/Delving-into-Crispness
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ECCV 2018
Recent methods for boundary or edge detection built on Deep Convolutional Neural Networks (CNNs) typically suffer from the issue of predicted edges being thick and need post-processing to obtain crisp boundaries.
