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Related Experiment Video

Updated: May 29, 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

A Fast Correlation Method for Scale-and Translation-Invariant Pattern Recognition.

J Altmann1, H J Reitbock

  • 1Arbeitsgruppe Angewandte Physik und Experimentelle Biophysik, Philipps-Universität Marburg, D-3550 Marburg, West Germany.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
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This study proposes a new correlation method for image analysis, overcoming limitations of the discrete Fourier-Mellin transform. The method achieves size and position invariance for reliable object recognition and scaling estimation.

Area of Science:

  • Image processing
  • Computer vision
  • Pattern recognition

Background:

  • Traditional Mellin transform methods for invariant image description suffer from sampling and border effects in digital implementations.
  • Discrete Fourier-Mellin transform (DFMT) does not perfectly preserve exact invariances due to digital processing limitations.

Purpose of the Study:

  • To address the limitations of DFMT in achieving size- and position-invariant image descriptions.
  • To propose and evaluate an alternative correlation method for robust image analysis.

Main Methods:

  • Calculating the normalized absolute magnitude of the discrete Fourier transform (DFT) for translation and amplitude invariance.
  • Applying logarithmic distortion to convert scaling into translation.
  • Comparing two transform methods using normalized Euclidean distances to identify similarity under translation and scaling.

Related Experiment Videos

Last Updated: May 29, 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

Main Results:

  • The proposed correlation method demonstrates potential for distinguishing between similar and dissimilar objects based on translation and scaling.
  • The method's effectiveness is contingent on specific size criteria related to the DFT, namely object size not exceeding N/4 and object details being larger than approximately 4 image points.
  • The magnitude of the shift in the comparison correlates with the scale factor between objects.

Conclusions:

  • The developed correlation method offers a viable alternative to DFMT for achieving size- and position-invariant image analysis.
  • Adherence to specified object size constraints is crucial for successful application of the method.
  • The method's performance is influenced by image complexity and the desired range of scale variations.