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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Published on: August 30, 2013

Multi-scale image edge detection based on spatial-frequency domain interactive attention.

Yongfei Guo¹, Bo Li¹, Wenyue Zhang¹

¹Xi'an Jieda Measurement & Control Co., Ltd., Xi'an, China.

Frontiers in Neurorobotics

|May 13, 2025

Summary

This summary is machine-generated.

This study introduces a novel multi-scale edge detection network using spatial-frequency domain interactive attention. It improves accurate edge detection in complex backgrounds, outperforming existing methods.

Keywords:

edge detection frequency domain interactive attention multiple scale spatial domain

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Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Edge detection is crucial in computer vision but challenging with complex backgrounds.
Existing deep learning methods struggle with small targets and intricate backgrounds.

Purpose of the Study:

To develop a multi-scale edge detection network for accurate target edge identification.
To address limitations of current methods in complex visual environments.

Main Methods:

Proposed a novel multi-scale edge detection network.
Introduced a spatial-frequency domain interactive attention module.
Leveraged frequency domain filtering and spatial-frequency interaction for feature extraction.

Main Results:

Achieved superior performance indicators compared to existing edge detection networks.
Demonstrated enhanced output image quality.
Successfully filtered background interference for clearer edge detection.

Conclusions:

The proposed network effectively detects edges of main targets across multiple scales.
Spatial-frequency domain interaction enhances accuracy in complex scenes.
The method offers a significant advancement in computer vision edge detection.