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Noise-Robust image edge detection based on multi-scale automatic anisotropic morphological Gaussian Kernels.

Lei Liang1, Junming Chen2, Jiawei Shi3

  • 1College of Arts, Nanjing University of Information Science and Technology, Nanjing, China.

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|May 5, 2025
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Summary
This summary is machine-generated.

This study introduces a novel edge detection method that improves noise robustness and accuracy. It enhances the Canny detector to overcome cross-edge detection failures, offering superior performance in image analysis.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Edge detection is crucial for image analysis.
  • Existing methods like Canny struggle with noise and complex edge structures.
  • Cross-edge detection failure limits current algorithms.

Purpose of the Study:

  • To develop a novel multi-scale, noise-robust edge detection method.
  • To address and overcome the cross-edge detection failure of the Canny edge detector.
  • To improve edge resolution and accuracy in noisy images.

Main Methods:

  • Proposed a multi-scale automatic anisotropic morphological directional derivative (AMDD) for capturing local gray-level variations.
  • Introduced a fused edge strength map (ESM) based on multi-scale AMDD.
  • Integrated fused ESMs and edge direction maps (EDMs) into the Canny framework using spatial and directional matching filters.

Main Results:

  • The proposed method demonstrates superior noise robustness and edge accuracy compared to existing detectors.
  • Effectively reduces noise and irrelevant signal interference.
  • Achieved competitive performance on a standard dataset, validated by precision-recall curves and Pratt's Figure of Merit.

Conclusions:

  • The novel edge detection method offers significant advantages in noise robustness and edge accuracy.
  • Successfully mitigates limitations of the Canny detector, particularly with cross edges.
  • Provides a more reliable approach for edge detection in challenging image conditions.