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Related Concept Videos

Ordinal Level of Measurement00:55

Ordinal Level of Measurement

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Mapping images into ordinal networks.

Arthur A B Pessa1, Haroldo V Ribeiro1

  • 1Departamento de Física, Universidade Estadual de Maringá - Maringá, PR 87020-900, Brazil.

Physical Review. E
|December 17, 2020
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Summary
This summary is machine-generated.

We introduce a generalized ordinal network algorithm to map images into networks, enabling analysis of higher-dimensional data. This method effectively captures image properties like roughness and symmetry for improved texture classification.

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Area of Science:

  • Network Science
  • Image Analysis
  • Data Mining

Background:

  • Recent network science research uses abstraction to map complex data into networks.
  • Existing methods primarily focus on one-dimensional time series data.
  • Extending network analysis to higher-dimensional data like images remains a challenge.

Purpose of the Study:

  • To generalize the ordinal network algorithm for mapping images into networks.
  • To investigate connectivity constraints in ordinal networks derived from images.
  • To establish a framework for analyzing image properties using network measures.

Main Methods:

  • Generalization of the ordinal network algorithm for image data.
  • Analysis of connectivity constraints arising from symbolization.
  • Derivation of the exact structure of ordinal networks from random images.
  • Application to periodic ornaments, fractional Brownian motion, Ising model, and natural textures.

Main Results:

  • Ordinal networks successfully map image properties.
  • Measures like average shortest path and global node entropy capture roughness and symmetry.
  • The method demonstrates robustness against noise.
  • Ordinal network measures outperform traditional texture descriptors in classification tasks.

Conclusions:

  • The generalized ordinal network algorithm provides a novel approach for image analysis.
  • Network-derived image properties are valuable for texture classification and understanding image complexity.
  • This method opens new avenues for applying network science to high-dimensional data.