Edge Detection
115 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 implementationsMost implemented papers
Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection
This dataset has been used for training the proposed approach as well the state-of-the-art algorithms for comparisons.
On Detection of Faint Edges in Noisy Images
A fundamental question for edge detection in noisy images is how faint can an edge be and still be detected.
Faster Training of Mask R-CNN by Focusing on Instance Boundaries
We present an auxiliary task to Mask R-CNN, an instance segmentation network, which leads to faster training of the mask head.
DeepFlux for Skeletons in the Wild
In the present article, we depart from this strategy by training a CNN to predict a two-dimensional vector field, which maps each scene point to a candidate skeleton pixel, in the spirit of flux-based skeletonization algorithms.
Bi-Directional Cascade Network for Perceptual Edge Detection
Exploiting multi-scale representations is critical to improve edge detection for objects at different scales.
Object Contour and Edge Detection with RefineContourNet
A ResNet-based multi-path refinement CNN is used for object contour detection.
Edge-Direct Visual Odometry
In contrast our method builds on direct visual odometry methods naturally with minimal added computation.
A novel centroid update approach for clustering-based superpixel methods and superpixel-based edge detection
Then according to the statistical features of noise, we propose a novel centroid update approach to enhance the robustness of clustering-based superpixel methods.
Pixel Difference Networks for Efficient Edge Detection
A faster version of PiDiNet with less than 0. 1M parameters can still achieve comparable performance among state of the arts with 200 FPS.
SILOP: An Automated Framework for Semantic Segmentation Using Image Labels Based on Object Perimeters
Our new PerimeterFit module will be applied to pre-refine the CAM predictions before using the pixel-similarity-based network.