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 251–275 of 490 papers

TitleStatusHype
Empirical Study of Multi-Task Hourglass Model for Semantic Segmentation TaskCode0
Edge Detection for Satellite Images without Deep Networks—0
A Novel Falling-Ball Algorithm for Image Segmentation—0
Equivalence of Correlation Filter and Convolution Filter in Visual Tracking—0
Neuro-inspired edge feature fusion using Choquet integralsCode0
EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks—0
Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data—0
A Systematic Approach for MRI Brain Tumor Localization, and Segmentation using Deep Learning and Active Contouring—0
Classic versus deep learning approaches to address computer vision challenges—0
A relic sketch extraction framework based on detail-aware hierarchical deep network—0
Shape Back-Projection In 3D Scenes—0
Context-Aware Image Denoising with Auto-Threshold Canny Edge Detection to Suppress Adversarial Perturbation—0
Gaussian Mixture Graphical Lasso with Application to Edge Detection in Brain Networks—0
Combining Deep Learning and Mathematical Morphology for Historical Map Segmentation—0
Identifying centres of interest in paintings using alignment and edge detection: Case studies on works by Luc Tuymans—0
Detection algorithm of defects on polyethylene gas pipe using image recognition—0
Can Shape Structure Features Improve Model Robustness Under Diverse Adversarial Settings?—0
Revisiting Edge Detection in Convolutional Neural Networks—0
A Feasibility study for Deep learning based automated brain tumor segmentation using Magnetic Resonance Images—0
TEMImageNet Training Library and AtomSegNet Deep-Learning Models for High-Precision Atom Segmentation, Localization, Denoising, and Super-Resolution Processing of Atomic-Resolution Images—0
Sylvester Matrix Based Similarity Estimation Method for Automation of Defect Detection in Textile Fabrics—0
Segmentation overlapping wear particles with few labelled data and imbalance sample—0
A Simple and Efficient Registration of 3D Point Cloud and Image Data for Indoor Mobile Mapping System—0
Application of sequential processing of computer vision methods for solving the problem of detecting the edges of a honeycomb block—0
Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate ControlCode0
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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
2PiDiNetODS0.79—Unverified
3BDCNODS0.79—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