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Noise injection into Freeman chain codes.

Luka Lukač1, Andrej Nerat1, Damjan Strnad1

  • 1Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia.

Peerj. Computer Science
|September 24, 2025
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Summary
This summary is machine-generated.

This study introduces a new method for adding noise directly to shape representations called Freeman chain codes. This technique enhances data for neural network training while preserving shape integrity.

Keywords:
AlgorithmBoundary alterationFractal DimensionGeometric shape

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Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Geometry

Background:

  • Freeman chain codes are a common method for shape representation.
  • Existing noise injection methods may alter topological properties.
  • Data augmentation is crucial for robust neural network training.

Purpose of the Study:

  • To develop a novel method for direct noise injection into Freeman chain codes.
  • To ensure injected noise preserves the topological characteristics of shapes.
  • To facilitate data augmentation and regularization in neural network training.

Main Methods:

  • Noise is injected into randomly selected segments of Freeman chain code sequences.
  • A set of predefined actions is used to alter the chain codes.
  • Fractal dimension and mean distance from the original are used for analysis.

Main Results:

  • The method successfully injects noise into various shape types, including those with holes.
  • Topological characteristics of the shapes are retained after noise injection.
  • Quantitative analysis confirms the control over the amount of injected noise.

Conclusions:

  • The proposed method offers an efficient way to introduce noise directly into Freeman chain codes.
  • This approach is valuable for improving the robustness of models trained with augmented data.
  • The technique supports diverse shape representations and complex topological features.