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

Showing 125 of 490 papers

TitleStatusHype
OmniGen: Unified Image GenerationCode7
Lightweight Pixel Difference Networks for Efficient Visual Representation LearningCode4
DiffusionEdge: Diffusion Probabilistic Model for Crisp Edge DetectionCode3
Rethinking Boundary Detection in Deep Learning-Based Medical Image SegmentationCode2
Visual Prompting via Image InpaintingCode2
Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing ImageryCode2
Low-Light Image Enhancement via Structure Modeling and GuidanceCode2
Tiny and Efficient Model for the Edge Detection GeneralizationCode2
EDTER: Edge Detection with TransformerCode2
Generation of Realistic Synthetic Raw Radar Data for Automated Driving Applications using Generative Adversarial NetworksCode1
Euler: Detecting Network Lateral Movement via Scalable Temporal Link PredictionCode1
EdgeNAT: Transformer for Efficient Edge DetectionCode1
Adaptive Linear Span Network for Object Skeleton DetectionCode1
FINED: Fast Inference Network for Edge DetectionCode1
Extraction of cropland field parcels with high resolution remote sensing using multi-task learningCode1
Geometric Structure Preserving Warp for Natural Image StitchingCode1
ECT: Fine-grained Edge Detection with Learned Cause TokensCode1
Dense Extreme Inception Network for Edge DetectionCode1
Delving into Crispness: Guided Label Refinement for Crisp Edge DetectionCode1
Dense Extreme Inception Network: Towards a Robust CNN Model for Edge DetectionCode1
Edge Augmentation for Large-Scale Sketch Recognition without SketchesCode1
Dual Attention U-Net with Feature Infusion: Pushing the Boundaries of Multiclass Defect SegmentationCode1
A Doubly Decoupled Network for edge detectionCode1
EDMB: Edge Detector with MambaCode1
Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image AttenuationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DDNODS0.92Unverified
2DexiNedODS0.9Unverified
3BDCNODS0.89Unverified
4LDCODS0.89Unverified
5CATSODS0.89Unverified
6RCFODS0.85Unverified
#ModelMetricClaimedVerifiedStatus
1DexiNed-aODS0.89Unverified
2DexiNed-fODS0.89Unverified
3CATSODS0.89Unverified
4BDCNODS0.89Unverified
5LDCODS0.88Unverified
6RCFODS0.88Unverified
#ModelMetricClaimedVerifiedStatus
1DDNODS0.83Unverified
2TEEDODS0.83Unverified
3LDCODS0.82Unverified
4DexiNedODS0.82Unverified
5PiDiNetODS0.81Unverified
#ModelMetricClaimedVerifiedStatus
1LDCODS0.79Unverified
2BDCNODS0.79Unverified
3PiDiNetODS0.79Unverified
#ModelMetricClaimedVerifiedStatus
1SEDODS0.65Unverified
2DexiNed (WACV'2020)ODS0.65Unverified
#ModelMetricClaimedVerifiedStatus
1RPCNetAP86.15Unverified
2CASENetAP70.8Unverified
#ModelMetricClaimedVerifiedStatus
1CASENetMaximum F-measure71.4Unverified
2WSOBMaximum F-measure52Unverified
#ModelMetricClaimedVerifiedStatus
1RCNF10.82Unverified