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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Radial and angular moment invariants for image identification.

S S Reddi1

  • 1Aeronutronic Division, Aerospace and Communication Corporation, Newport Beach, CA 92663.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary

This study introduces novel methods for creating image moment functions invariant to transformations like rotation and scaling. These functions simplify the visual assessment of image properties, enhancing pattern recognition capabilities.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Image analysis often requires features invariant to geometric transformations.
  • Traditional methods for achieving invariance can be complex and computationally intensive.

Purpose of the Study:

  • To present methods for deriving radial and angular image moments.
  • To demonstrate how these moments can be used to create functions invariant to rotation, translation, reflection, and size changes.
  • To express Hu's invariants using these moment functions for easier visual inspection.

Main Methods:

  • Calculation of radial and angular moments from image data.
  • Development of moment functions that exhibit invariance properties without relying on algebraic invariant theory.
  • Formulation of Hu's invariants in terms of the derived radial and angular moments.

Main Results:

  • Successfully derived moment functions invariant to common image transformations.
  • Demonstrated a method to express Hu's invariants using these novel moment functions.
  • Showcased the facilitation of visual inspection of invariance properties.

Conclusions:

  • The proposed methods provide a straightforward approach to achieving image invariance.
  • Expressing Hu's invariants via radial and angular moments simplifies their analysis and application.
  • This technique offers a valuable tool for image analysis and pattern recognition tasks.