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Subpixel Localization of Isolated Edges and Streaks in Digital Images.

Devin T Renshaw1, John A Christian1

  • 1Department of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.

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|August 30, 2021
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Summary

This study presents a new framework for precise subpixel edge and streak localization in digital images. The Zernike moments method offers superior performance over existing techniques, especially in noisy image conditions.

Keywords:
Zernike momentsedge localizationimage processingstreak localizationsubpixel

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Area of Science:

  • Digital Image Processing
  • Computer Vision
  • Computational Imaging

Background:

  • Accurate measurement data extraction from digital images is crucial for modern sensing systems.
  • Edge and streak localization are key techniques in image processing pipelines, with subpixel methods offering higher accuracy.
  • Existing subpixel methods often suffer from noise sensitivity, iterative processes, or pre-processing requirements.

Purpose of the Study:

  • To investigate a unified framework for subpixel edge and streak localization.
  • To utilize Zernike moments with ramp-based and wedge-based signal models for improved localization accuracy.
  • To address limitations of current subpixel localization techniques.

Main Methods:

  • Development of a unified framework for subpixel edge and streak localization.
  • Application of Zernike moments with ramp-based and wedge-based signal models.
  • Evaluation using both synthetic and real digital images.

Main Results:

  • The proposed Zernike moments-based method outperforms current state-of-the-art techniques.
  • Effective performance demonstrated on digital images with common signal-to-noise ratios.
  • Validation across both synthetic and real-world image datasets.

Conclusions:

  • The unified framework using Zernike moments provides a robust and accurate solution for subpixel edge and streak localization.
  • This method is particularly effective in handling noisy image data.
  • The approach offers a significant advancement for image processing applications demanding high localization precision.