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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Morphological autocorrelation transform: A new representation and classification scheme for two-dimensional images.

A P Loui1, A N Venetsanopoulos, K C Smith

  • 1Dept. of Electr. Eng., Toronto Univ., Ont.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1992
PubMed
Summary

This study introduces the morphological autocorrelation transform (MAT) for efficient 2D shape recognition. The MAT descriptor is invariant to translation, scale, and rotation, enabling robust shape analysis and classification.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Efficient recognition of 2D objects is crucial in various fields.
  • Existing shape descriptors may lack invariance to common transformations.

Purpose of the Study:

  • To propose a novel, efficient methodology for 2D shape recognition.
  • To introduce and analyze the morphological autocorrelation transform (MAT) as a shape descriptor.

Main Methods:

  • Developed a methodology based on mathematical morphology.
  • Introduced the morphological autocorrelation transform (MAT) as a shape descriptor.
  • Analyzed the invariance properties (translation, scale, rotation) of the MAT.

Main Results:

  • The MAT is composed of geometrical correlation functions (GCFs) defining morphological covariance.
  • A small subset of the MAT is often sufficient for image representation.
  • Shape properties like area, perimeter, and orientation are derivable from the MAT.

Conclusions:

  • The proposed MAT-based system offers efficient and invariant 2D shape representation.
  • The system is well-suited for shape classification tasks.
  • Computational complexity analysis indicates practical applicability.