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 401–425 of 490 papers

TitleStatusHype
Quantum spectral analysis: frequency in time, with applications to signal and image processing—0
Quark: A Gradient-Free Quantum Learning Framework for Classification Tasks—0
SEMEDA: Enhancing Segmentation Precision with Semantic Edge Aware Loss—0
R-C-P Method: An Autonomous Volume Calculation Method Using Image Processing and Machine Vision—0
Real-Time and Efficient Method for Accuracy Enhancement of Edge Based License Plate Recognition System—0
Real-Time Asphalt Pavement Layer Thickness Prediction Using Ground-Penetrating Radar Based on a Modified Extended Common Mid-Point (XCMP) Approach—0
Real Time Incremental Image Mosaicking Without Use of Any Camera Parameter—0
Real-time Lane detection and Motion Planning in Raspberry Pi and Arduino for an Autonomous Vehicle Prototype—0
Recognizing License Plates in Real-Time—0
Reconstruction-driven Dynamic Refinement based Unsupervised Domain Adaptation for Joint Optic Disc and Cup Segmentation—0
Floor-SP: Inverse CAD for Floorplans by Sequential Room-wise Shortest PathCode0
Feature Pyramid and Hierarchical Boosting Network for Pavement Crack DetectionCode0
Semantic Edge Detection with Diverse Deep SupervisionCode0
Holistically-Nested Edge DetectionCode0
A novel centroid update approach for clustering-based superpixel methods and superpixel-based edge detectionCode0
semantic image synthesis of anime characters based on conditional generative adversarial networksCode0
Object Contour and Edge Detection with RefineContourNetCode0
Object Contour Detection with a Fully Convolutional Encoder-Decoder NetworkCode0
XAI-TRIS: Non-linear image benchmarks to quantify false positive post-hoc attribution of feature importanceCode0
DeepFlux for Skeletons in the WildCode0
On Detection of Faint Edges in Noisy ImagesCode0
STEdge: Self-training Edge Detection with Multi-layer Teaching and RegularizationCode0
SILOP: An Automated Framework for Semantic Segmentation Using Image Labels Based on Object PerimetersCode0
On Image segmentation using Fractional Gradients-Learning Model Parameters using Approximate Marginal InferenceCode0
On-Off Center-Surround Receptive Fields for Accurate and Robust Image ClassificationCode0
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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