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 326–350 of 490 papers

TitleStatusHype
Hyb-KAN ViT: Hybrid Kolmogorov-Arnold Networks Augmented Vision Transformer—0
Hybrid algorithm for the detection of turbulent flame fronts—0
Hybrid Multi-Stage Learning Framework for Edge Detection: A Survey—0
Hybrid quantum transfer learning for crack image classification on NISQ hardware—0
IDAN: Image Difference Attention Network for Change Detection—0
Identification and Counting White Blood Cells and Red Blood Cells using Image Processing Case Study of Leukemia—0
Identifying centres of interest in paintings using alignment and edge detection: Case studies on works by Luc Tuymans—0
Image Processing Failure and Deep Learning Success in Lawn Measurement—0
Groupwise Image Registration with Edge-Based Loss for Low-SNR Cardiac MRI—0
Image Segmentation Algorithms Overview—0
Image Segmentation Based on Watershed and Edge Detection Techniques—0
InstanceCut: from Edges to Instances with MultiCut—0
Iris: Breaking GUI Complexity with Adaptive Focus and Self-Refining—0
Is Image Super-resolution Helpful for Other Vision Tasks?—0
Joint Semantic Segmentation and Boundary Detection using Iterative Pyramid Contexts—0
Kernel-Based Structural Equation Models for Topology Identification of Directed Networks—0
Lane Detection For Prototype Autonomous Vehicle—0
Learning a microlocal prior for limited-angle tomography—0
Learning Contour-Fragment-based Shape Model with And-Or Tree Representation—0
Learning Crisp Edge Detector Using Logical Refinement Network—0
Learning Informative Edge Maps for Indoor Scene Layout Prediction—0
Learning Multiple Representations with Inconsistency-Guided Detail Regularization for Mask-Guided Matting—0
Learning Pixel Representations for Generic Segmentation—0
Learning Relaxed Deep Supervision for Better Edge Detection—0
Learning to utilize image second-order derivative information for crisp edge detection—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