SOTAVerified

Edge Detection

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 )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 76–100 of 490 papers

TitleStatusHype
The Effectiveness of Edge Detection Evaluation Metrics for Automated Coastline DetectionCode0
Automated Coastline Extraction Using Edge Detection AlgorithmsCode0
NAFRSSR: a Lightweight Recursive Network for Efficient Stereo Image Super-ResolutionCode0
AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language ModelsCode1
Statistical Edge Detection And UDF Learning For Shape Representation—0
Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing ImageryCode2
Leveraging edge detection and neural networks for better UAV localizationCode1
Msmsfnet: a multi-stream and multi-scale fusion net for edge detection—0
Learning Multiple Representations with Inconsistency-Guided Detail Regularization for Mask-Guided Matting—0
CT-Bound: Robust Boundary Detection From Noisy Images Via Hybrid Convolution and Transformer Neural NetworksCode0
An edge detection-based deep learning approach for tear meniscus height measurement—0
Advanced Knowledge Extraction of Physical Design Drawings, Translation and conversion to CAD formats using Deep Learning—0
Texture Edge detection by Patch consensus (TEP)—0
AFGI: Towards Accurate and Fast-convergent Gradient Inversion Attack in Federated LearningCode0
MGIC: A Multi-Label Gradient Inversion Attack based on Canny Edge Detection on Federated Learning—0
RankED: Addressing Imbalance and Uncertainty in Edge Detection Using Ranking-based LossesCode1
CDSE-UNet: Enhancing COVID-19 CT Image Segmentation with Canny Edge Detection and Dual-Path SENet Feature Fusion—0
On the Accuracy of Edge Detectors in Number Plate Extraction—0
Lightweight, error-tolerant edge detection using memristor-enabled stochastic logics—0
SHM-Traffic: DRL and Transfer learning based UAV Control for Structural Health Monitoring of Bridges with Traffic—0
Color Recognition in Challenging Lighting Environments: CNN Approach—0
Lightweight Pixel Difference Networks for Efficient Visual Representation LearningCode4
Applications of Tao General Difference in Discrete Domain—0
Real-Time Asphalt Pavement Layer Thickness Prediction Using Ground-Penetrating Radar Based on a Modified Extended Common Mid-Point (XCMP) Approach—0
Systematic review of image segmentation using complex networks—0
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All datasetsBIPEDMDBDUDEDBRINDCIDCityscapes testSBDBSDS500

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DDNODS0.92—Unverified
2DexiNedODS0.9—Unverified
3BDCNODS0.89—Unverified
4LDCODS0.89—Unverified
5CATSODS0.89—Unverified
6RCFODS0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1DexiNed-aODS0.89—Unverified
2DexiNed-fODS0.89—Unverified
3CATSODS0.89—Unverified
4BDCNODS0.89—Unverified
5LDCODS0.88—Unverified
6RCFODS0.88—Unverified
#ModelMetricClaimedVerifiedStatus
1DDNODS0.83—Unverified
2TEEDODS0.83—Unverified
3LDCODS0.82—Unverified
4DexiNedODS0.82—Unverified
5PiDiNetODS0.81—Unverified
#ModelMetricClaimedVerifiedStatus
1LDCODS0.79—Unverified
2BDCNODS0.79—Unverified
3PiDiNetODS0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1SEDODS0.65—Unverified
2DexiNed (WACV'2020)ODS0.65—Unverified
#ModelMetricClaimedVerifiedStatus
1RPCNetAP86.15—Unverified
2CASENetAP70.8—Unverified
#ModelMetricClaimedVerifiedStatus
1CASENetMaximum F-measure71.4—Unverified
2WSOBMaximum F-measure52—Unverified
#ModelMetricClaimedVerifiedStatus
1RCNF10.82—Unverified