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A Fast Circle Detection Algorithm Based on Information Compression.

Yun Ou1, Honggui Deng1, Yang Liu1

  • 1School of Physics and Electronics, Central South University, Lushan South Road, Changsha 410083, China.

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|October 14, 2022
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Summary
This summary is machine-generated.

This study introduces a fast circle detection algorithm using information compression. The novel method significantly improves precision, recall, and speed compared to existing techniques.

Keywords:
average sampling verificationcircle detectioninformation compression

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Area of Science:

  • Computer Vision
  • Image Processing

Background:

  • Circle detection is crucial in computer vision but traditional algorithms are slow and susceptible to noise.
  • Existing methods often struggle with efficiency and robustness.

Purpose of the Study:

  • To develop a fast and noise-robust circle detection algorithm.
  • To improve upon the limitations of conventional circle detection methods.

Main Methods:

  • Information compression to reduce image data while preserving circular features.
  • Sharpness estimation and orientation filtering to remove noise.
  • An O(1) average sampling algorithm for efficient candidate circle generation.
  • Constrained analysis for identifying true circles from candidates.

Main Results:

  • The proposed method compresses circular information to 1% of original data points.
  • Significant improvements in Precision, Recall, Time, and F-measure were observed.
  • Outperformed established algorithms like RHT, RCD, Jiang, Wang, and CACD.

Conclusions:

  • The information compression-based approach offers a highly efficient and accurate solution for circle detection.
  • This method effectively addresses the speed and noise sensitivity issues of prior algorithms.