Edge Detection
118 papers with code • 8 benchmarks • 9 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Edge Detection
Libraries
Use these libraries to find Edge Detection models and implementations
Cityscapes
BSDS500
SBD
BIPED
MDBD
UDED
INRIA-Horse
Latest papers
Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagery
The rapid advancement of automated artificial intelligence algorithms and remote sensing instruments has benefited change detection (CD) tasks.
Leveraging edge detection and neural networks for better UAV localization
TheoDpPro/uav-localization
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Here, we demonstrate that the performance of these methods can be significantly enhanced by preprocessing the images to extract their edges, which exhibit robustness to seasonal and illumination variations.
Colour and Brush Stroke Pattern Recognition in Abstract Art using Modified Deep Convolutional Generative Adversarial Networks
Deceptrax123/Pattern-Recognition-in-Abstract-art-
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Further this paper explores the generated latent space by performing random walks to understand vector relationships between brush strokes and colours in the abstract art space and a statistical analysis of unstable outputs after a certain period of GAN training and compare its significant difference.
RAF-GI: Towards Robust, Accurate and Fast-Convergent Gradient Inversion Attack in Federated Learning
Yet, FL users are susceptible to the gradient inversion (GI) attack which can reconstruct ground-truth training data such as images based on model gradients.
Lightweight Pixel Difference Networks for Efficient Visual Representation Learning
With PDC and Bi-PDC, we further present two lightweight deep networks named \emph{Pixel Difference Networks (PiDiNet)} and \emph{Binary PiDiNet (Bi-PiDiNet)} respectively to learn highly efficient yet more accurate representations for visual tasks including edge detection and object recognition.
DiffusionEdge: Diffusion Probabilistic Model for Crisp Edge Detection
With the recent success of the diffusion probabilistic model (DPM), we found it is especially suitable for accurate and crisp edge detection since the denoising process is directly applied to the original image size.
Dual Attention U-Net with Feature Infusion: Pushing the Boundaries of Multiclass Defect Segmentation
The proposed architecture, Dual Attentive U-Net with Feature Infusion (DAU-FI Net), addresses challenges in semantic segmentation, particularly on multiclass imbalanced datasets with limited samples.
Deep transfer learning for visual analysis and attribution of paintings by Raphael
Visual analysis and authentication of artworks are challenging tasks central to art history and criticism.
Meta ControlNet: Enhancing Task Adaptation via Meta Learning
junjieyang97/meta-controlnet
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However, vanilla ControlNet generally requires extensive training of around 5000 steps to achieve a desirable control for a single task.
DiffSLVA: Harnessing Diffusion Models for Sign Language Video Anonymization
While signers have expressed interest, for a variety of applications, in sign language video anonymization that would effectively preserve linguistic content, attempts to develop such technology have had limited success, given the complexity of hand movements and facial expressions.
