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 with no code
Msmsfnet: a multi-stream and multi-scale fusion net for edge detection
In this work, we study the performance that can be achieved by state-of-the-art deep learning based edge detectors in publicly available datasets when they are trained from scratch, and devise a new network architecture, the multi-stream and multi scale fusion net (msmsfnet), for edge detection.
Learning Multiple Representations with Inconsistency-Guided Detail Regularization for Mask-Guided Matting
Our framework and model introduce the following key aspects: (1) to learn real-world adaptive semantic representation for objects with diverse and complex structures under real-world scenes, we introduce extra semantic segmentation and edge detection tasks on more diverse real-world data with segmentation annotations; (2) to avoid overfitting on low-level details, we propose a module to utilize the inconsistency between learned segmentation and matting representations to regularize detail refinement; (3) we propose a novel background line detection task into our auxiliary learning framework, to suppress interference of background lines or textures.
An edge detection-based deep learning approach for tear meniscus height measurement
For improved segmentation of the pupil and tear meniscus areas, the convolutional neural network Inceptionv3 was first implemented as an image quality assessment model, effectively identifying higher-quality images with an accuracy of 98. 224%.
Advanced Knowledge Extraction of Physical Design Drawings, Translation and conversion to CAD formats using Deep Learning
The approach employs object detection model, such as Yolov7, Faster R-CNN, to detect physical drawing objects present in the images followed by, edge detection algorithms such as canny filter to extract and refine the identified lines from the drawing region and curve detection techniques to detect circle.
Texture Edge detection by Patch consensus (TEP)
We propose Texture Edge detection using Patch consensus (TEP) which is a training-free method to detect the boundary of texture.
MGIC: A Multi-Label Gradient Inversion Attack based on Canny Edge Detection on Federated Learning
As a new distributed computing framework that can protect data privacy, federated learning (FL) has attracted more and more attention in recent years.
RankED: Addressing Imbalance and Uncertainty in Edge Detection Using Ranking-based Losses
Detecting edges in images suffers from the problems of (P1) heavy imbalance between positive and negative classes as well as (P2) label uncertainty owing to disagreement between different annotators.
CDSE-UNet: Enhancing COVID-19 CT Image Segmentation with Canny Edge Detection and Dual-Path SENet Feature Fusion
Accurate segmentation of COVID-19 CT images is crucial for reducing the severity and mortality rates associated with COVID-19 infections.
On the Accuracy of Edge Detectors in Number Plate Extraction
Edge detection as a pre-processing stage is a fundamental and important aspect of the number plate extraction system.
Lightweight, error-tolerant edge detection using memristor-enabled stochastic logics
The demand for efficient edge vision has spurred the interest in developing stochastic computing approaches for performing image processing tasks.
