High-Precision Edge Detection Guided byFlow Fields
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View abstract on PubMed
Summary
This summary is machine-generated.We introduce FFED, a novel flow field-guided edge detection model. FFED enhances complex target extraction and background information capture for improved visual tasks.
Area Of Science
- Computer Vision
- Image Processing
- Artificial Intelligence
Background
- Edge detection is crucial for visual tasks but struggles with complex textures and backgrounds.
- Existing methods face challenges in extracting detailed information from intricate scenes.
Purpose Of The Study
- To propose FFED, a flow field-guided edge detection model.
- To address limitations in current edge detection techniques for complex targets and backgrounds.
Main Methods
- FFED utilizes a Feature Broadcast Module (FBM) to transfer semantic features to high-resolution details.
- An Antagonistic Bio-inspired Spatial Attention Module (ABSAM) optimizes spatial modeling for complex backgrounds.
- A novel pixel difference convolution, ALS, enhances gradient and subtle texture extraction.
Main Results
- FFED achieved competitive results on benchmark datasets (NYUD, BSDS500, BIPED) and industrial data.
- The model demonstrated effectiveness in extracting complex textured targets and background information.
- FFED showed a positive auxiliary effect on downstream visual tasks.
Conclusions
- FFED offers a robust solution for edge detection in complex visual scenes.
- The proposed modules (FBM, ABSAM, ALS) significantly improve edge detection performance.
- FFED advances the capabilities of edge detection for various computer vision applications.
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