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 101–125 of 490 papers

TitleStatusHype
Clutter Edges Detection Algorithms for Structured Clutter Covariance Matrices—0
Comparing Edge Detection Methods based on Stochastic Entropies and Distances for PolSAR Imagery—0
An image structure model for exact edge detection—0
A Fingerprint-based Access Control using Principal Component Analysis and Edge Detection—0
Computing the Spatial Probability of Inclusion inside Partial Contours for Computer Vision Applications—0
Content Based Image Indexing and Retrieval—0
Context-Aware Image Denoising with Auto-Threshold Canny Edge Detection to Suppress Adversarial Perturbation—0
Context-Constrained Accurate Contour Extraction for Occlusion Edge Detection—0
Contour Detection in Cassini ISS images based on Hierarchical Extreme Learning Machine and Dense Conditional Random Field—0
Contour Detection in Unstructured 3D Point Clouds—0
A fast numerical algorithm for finding all real solutions to a system of N nonlinear equations in a finite domain—0
Convex Analysis of Mixtures for Separating Non-negative Well-grounded Sources—0
Applications of Tao General Difference in Discrete Domain—0
Coupled Segmentation and Edge Learning via Dynamic Graph Propagation—0
Classic versus deep learning approaches to address computer vision challenges—0
Cross-Domain Evaluation of Edge Detection for Biomedical Event Extraction—0
A relic sketch extraction framework based on detail-aware hierarchical deep network—0
Custom Extended Sobel Filters—0
Cutting-Edge Detection of Fatigue in Drivers: A Comparative Study of Object Detection Models—0
Cycle Pixel Difference Network for Crisp Edge Detection—0
DADU: Dual Attention-based Deep Supervised UNet for Automated Semantic Segmentation of Cardiac Images—0
Dam Burst: A region-merging-based image segmentation method—0
DCSM 2.0: Deep Conditional Shape Models for Data Efficient Segmentation—0
A new Edge Detector Based on Parametric Surface Model: Regression Surface Descriptor—0
CFAR Edge Detector for Polarimetric SAR Images—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