[Web]
A deep learning-based edge detection algorithm using holistically-nested fully convolutional neural networks.
What is [Web]?
Holistically-Nested Edge Detection (HED) is a deep learning-based computer vision algorithm for detecting edges and object boundaries in images. It uses a fully convolutional neural network architecture with deep supervision to automatically learn hierarchical features, achieving state-of-the-art accuracy on benchmark datasets while maintaining fast processing speeds. The approach treats edge detection as an image-to-image prediction problem rather than a traditional classification task.
Computer vision researchers and practitioners working on edge detection, image segmentation, or boundary analysis tasks, particularly those interested in deep learning approaches to low-level vision problems.
HED provides significantly better accuracy than previous edge detection methods while maintaining practical processing speeds, thanks to its holistically-nested architecture that learns rich hierarchical representations through deep supervision. It represents a fundamental advancement in how edge detection is approached using deep learning.
Overview
code for Holistically-Nested Edge Detection
Use Cases
Best For
- Edge detection in natural images for computer vision research
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![[Web]](/_next/image?url=https%3A%2F%2Fgithub.com%2Fnagadomi.png%3Fsize%3D40&w=96&q=75){kind=small}
![[Web]](/_next/image?url=https%3A%2F%2Fgithub.com%2Fgoodfeli.png%3Fsize%3D40&w=96&q=75){kind=small}
