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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

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Published on: August 30, 2013

Spatially invariant image sequences.

J V Miller1, J B Farison, Y Shin

  • 1Dept. of Electr. and Comput. Eng., Michigan Univ., Dearborn, MI.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1992
PubMed
Summary
This summary is machine-generated.

This study introduces a mathematical model for linearly additive spatially invariant image sequences. A novel simultaneous diagonalization (SD) filter is presented to enhance desired features and suppress unwanted ones in image sequences.

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Area of Science:

  • Image processing and computer vision
  • Mathematical modeling
  • Signal processing

Background:

  • Image sequences often contain additive components with varying contributions.
  • Distinguishing objects with similar spatial but different spectral characteristics is challenging.
  • Existing methods may struggle to effectively isolate specific processes within image sequences.

Purpose of the Study:

  • To define and mathematically model linearly additive spatially invariant image sequences.
  • To develop a method for distinguishing objects based on their spectral signatures within image sequences.
  • To introduce a linear filter for emphasizing desired processes and suppressing undesired ones.

Main Methods:

  • Development of an explicit mathematical model for linearly additive spatially invariant image sequences.
  • Characterization of object signatures based on spectral variations across image sequences.
  • Formulation and derivation of the simultaneous diagonalization (SD) filter.

Main Results:

  • Objects with different spectral characteristics exhibit unique image sequence signatures.
  • The SD filter effectively calculates a single image emphasizing desired processes.
  • Undesired processes can be suppressed in the filtered image.

Conclusions:

  • The proposed mathematical model accurately describes linearly additive spatially invariant image sequences.
  • The SD filter provides a powerful tool for image analysis and feature extraction.
  • Spectral signatures derived from image sequences are valuable for object discrimination.