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 351–375 of 490 papers

TitleStatusHype
Leveraging Spatial Attention and Edge Context for Optimized Feature Selection in Visual Localization—0
Liver Segmentation from Multimodal Images using HED-Mask R-CNN—0
Lightweight, error-tolerant edge detection using memristor-enabled stochastic logics—0
LS-Net: Fast Single-Shot Line-Segment Detector—0
Malaria detection in Segmented Blood Cell using Convolutional Neural Networks and Canny Edge Detection—0
Mammogram Edge Detection Using Hybrid Soft Computing Methods—0
Method of detecting the defects of the end face of steel coils—0
MGIC: A Multi-Label Gradient Inversion Attack based on Canny Edge Detection on Federated Learning—0
Microwave Hilbert Transformer and its Applications in Real-time Analog Processing (RAP)—0
MIRE: Matched Implicit Neural Representations—0
Mobile Image Analysis Application for Mantoux Skin Test—0
Model-based Outdoor Performance Capture—0
Msmsfnet: a multi-stream and multi-scale fusion net for edge detection—0
MuGE: Multiple Granularity Edge Detection—0
Multi-Class Segmentation of Heterogeneous Areas in Biomedical and Environmental Images Based on the Assessment of Local Edge Density—0
Multi-scale Curve Detection on Surfaces—0
Multiscale edge detection and parametric shape modeling for boundary delineation in optoacoustic images—0
N^4 -Fields: Neural Network Nearest Neighbor Fields for Image Transforms—0
A Critical Connectivity Radius for Segmenting Randomly-Generated, High Dimensional Data Points—0
New Algorithms for Learning Incoherent and Overcomplete Dictionaries—0
New Method for Optimization of License Plate Recognition system with Use of Edge Detection and Connected Component—0
Novel and Fast Algorithm for Extracting License Plate Location Based on Edge Analysis—0
Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model—0
Object Boundary Detection and Classification with Image-level Labels—0
Occlusion Edge Detection in RGB-D Frames using Deep Convolutional 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