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

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Showing 1–10 of 490 papers

TitleStatusHype
DeepCS-TRD, a Deep Learning-based Cross-Section Tree Ring DetectorCode0
U-R-VEDA: Integrating UNET, Residual Links, Edge and Dual Attention, and Vision Transformer for Accurate Semantic Segmentation of CMRs—0
Mobile Image Analysis Application for Mantoux Skin Test—0
Self-supervised Feature Extraction for Enhanced Ball Detection on Soccer Robots—0
Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis—0
Tropical Geometry Based Edge Detection Using Min-Plus and Max-Plus Algebra—0
Hyb-KAN ViT: Hybrid Kolmogorov-Arnold Networks Augmented Vision Transformer—0
Rethinking Boundary Detection in Deep Learning-Based Medical Image SegmentationCode2
Seeing Heat with Color -- RGB-Only Wildfire Temperature Inference from SAM-Guided Multimodal Distillation using Radiometric Ground Truth—0
Edge Detection based on Channel Attention and Inter-region Independence Test—0
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All datasetsBIPEDMDBDUDEDBRINDCIDCityscapes testSBDBSDS500

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CASENetMaximum F-measure71.4—Unverified
2WSOBMaximum F-measure52—Unverified