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
Latest 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.
A Pseudo-Boolean Polynomials Approach for Image Edge Detection
We introduce a novel approach for image edge detection based on pseudo-Boolean polynomials for image patches.
Practical Edge Detection via Robust Collaborative Learning
Edge detection, as a core component in a wide range of visionoriented tasks, is to identify object boundaries and prominent edges in natural images.
Zero-Shot Edge Detection with SCESAME: Spectral Clustering-based Ensemble for Segment Anything Model Estimation
ymgw55/scesame
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This paper proposes a novel zero-shot edge detection with SCESAME, which stands for Spectral Clustering-based Ensemble for Segment Anything Model Estimation, based on the recently proposed Segment Anything Model (SAM).
Tiny and Efficient Model for the Edge Detection Generalization
xavysp/teed
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Operations such as edge detection, image enhancement, and super-resolution, provide the foundations for higher level image analysis.
ECT: Fine-grained Edge Detection with Learned Cause Tokens
To address these three issues, we propose a two-stage transformer-based network sequentially predicting generic edges and fine-grained edges, which has a global receptive field thanks to the attention mechanism.
MSECNet: Accurate and Robust Normal Estimation for 3D Point Clouds by Multi-Scale Edge Conditioning
MSECNet consists of a backbone network and a multi-scale edge conditioning (MSEC) stream.
MSECNet: Accurate and Robust Normal Estimation for 3D Point Clouds by Multi-Scale Edge Conditioning
MSECNet consists of a backbone network and a multi-scale edge conditioning (MSEC) stream.
Generation of Realistic Synthetic Raw Radar Data for Automated Driving Applications using Generative Adversarial Networks
eduardo-candioto-fidelis/raw-radar-data-generation
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The results have shown that the data is realistic in terms of coherent radar reflections of the motorcycle and background noise based on the comparison of chirps, the RA maps and the object detection results.
Multispectral Image Segmentation in Agriculture: A Comprehensive Study on Fusion Approaches
Multispectral imagery is frequently incorporated into agricultural tasks, providing valuable support for applications such as image segmentation, crop monitoring, field robotics, and yield estimation.
