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 201–225 of 490 papers

TitleStatusHype
Enhanced Extractor-Selector Framework and Symmetrization Weighted Binary Cross-Entropy for Edge Detections—0
Comparative analysis of common edge detection techniques in context of object extraction—0
Directional Lifting Wavelet Transform for Image Edge Analysis—0
Enhancing Transparent Object Pose Estimation: A Fusion of GDR-Net and Edge Detection—0
Equivalence of Correlation Filter and Convolution Filter in Visual Tracking—0
Automated Quantification of White Blood Cells in Light Microscopic Images of Injured Skeletal Muscle—0
Eulerian Single-Photon Vision—0
Evaluating the Effectiveness of Automated Identity Masking (AIM) Methods with Human Perception and a Deep Convolutional Neural Network (CNN)—0
Exploring a Probabilistic Earley Parser for Event Composition in Biomedical Texts—0
Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis—0
Extracting Biomedical Event Using Feature Selection and Word Representation—0
Dilated filters for edge detection algorithms—0
Fall Leaf Adversarial Attack on Traffic Sign Classification—0
Content Based Image Indexing and Retrieval—0
Context-Aware Image Denoising with Auto-Threshold Canny Edge Detection to Suppress Adversarial Perturbation—0
Fast Edge Detection Using Structured Forests—0
Context-Constrained Accurate Contour Extraction for Occlusion Edge Detection—0
Identifying centres of interest in paintings using alignment and edge detection: Case studies on works by Luc Tuymans—0
FaultSeg Swin-UNETR: Transformer-Based Self-Supervised Pretraining Model for Fault Recognition—0
Feature Enhancement in Visually Impaired Images—0
Contour Detection in Unstructured 3D Point Clouds—0
Feature reconstruction from incomplete tomographic data without detour—0
Automated Management of Pothole related Disasters Using Image Processing and Geotagging—0
An approach to improving edge detection for facial and remotely sensed images using vector order statistics—0
An Adaptive Threshold for the Canny Edge Detection with Actor-Critic Algorithm—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