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Related Experiment Video

Updated: Jul 7, 2026

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

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Partial eigenvalue decomposition of large images using spatial temporal adaptive method.

W Murase1, M Lindenbaum

  • 1NTT Basic Res. Labs., Kanagawa.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1995
PubMed
Summary

This study introduces a spatial temporal adaptive (STA) method for efficient eigenvector computation from image sequences. The STA method significantly speeds up eigenvalue decomposition, making it practical for pattern recognition tasks.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Eigenvector computation for image sequences is computationally intensive.
  • Traditional methods limit practical applications in pattern recognition.

Purpose of the Study:

  • To develop a computationally efficient method for finding eigenvectors from real image sequences.
  • To enable practical application of eigenvector-based pattern recognition.

Main Methods:

  • Spatial temporal adaptive (STA) encoding to reduce storage and computation.
  • Singular Value Decomposition (SVD) applied after encoding.
  • Adaptive Discrete Cosine Transform (DCT) encoding with block consolidation for similar image regions.

Main Results:

Related Experiment Videos

Last Updated: Jul 7, 2026

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

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

  • The STA method is 6 to 10 times faster than traditional SVD.
  • Computational efficiency increases with image and inter-image correlation.
  • Verified on large, diverse image datasets.

Conclusions:

  • The STA method makes eigenvector decomposition practical for image sequences.
  • This advancement supports active research in eigenvector-based pattern recognition.