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Image edge detection based on singular value feature vector and gradient operator.

Jia Li Tang1, Yan Wang2, Chen Rong Huang3

  • 1College of Computer Engineering, Jiangsu University of Technology, Changzhou 213001, China.

Mathematical Biosciences and Engineering : MBE
|September 29, 2020
PubMed
Summary
This summary is machine-generated.

This novel edge detection algorithm uses singular value eigenvectors and gradient operators for improved image analysis. It demonstrates superior accuracy and efficiency in edge extraction, even with noise interference.

Keywords:
edge detectiongradient operatorsingular value feature vector

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Area of Science:

  • Computer Vision
  • Image Processing
  • Signal Processing

Background:

  • Edge detection is crucial for image analysis.
  • Existing methods face challenges with noise and accuracy.
  • Gradient operators are fundamental in edge detection.

Purpose of the Study:

  • To introduce a new edge detection algorithm.
  • To enhance accuracy and efficiency in edge extraction.
  • To improve noise resistance in image processing.

Main Methods:

  • Calculating singular values of image blocks.
  • Extending the Sobel gradient template to eight directions.
  • Determining pixel gradient values based on singular value stability.
  • Implementing a weighted function for global and local gradient threshold determination.

Main Results:

  • The algorithm effectively extracts edge information.
  • Experimental data show improved accuracy and efficiency compared to similar algorithms.
  • The proposed method exhibits robustness against noise interference.

Conclusions:

  • The singular value eigenvector and gradient operator-based algorithm offers superior performance.
  • This approach provides a promising solution for accurate and efficient edge detection.
  • The algorithm's noise resilience makes it suitable for real-world applications.