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Algorithms for shape analysis of contours and waveforms.

T Pavlidis1

  • 1FELLOW, IEEE, Department of Electrical Engineering and Computer Science, Princeton University, Princeton, NJ 08554; Bell Laboratories, Murray Hill, NJ 07974.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

This review classifies shape analysis algorithms based on whether they analyze boundaries or areas and use scalar or structural descriptions. Emphasis is placed on recent, information-preserving methods for image analysis.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Shape analysis is crucial for understanding image content.
  • Diverse algorithms exist, varying in scope (boundary vs. area) and representation (scalar vs. structural).
  • Recent advancements focus on information preservation.

Purpose of the Study:

  • To review and classify contemporary shape analysis algorithms.
  • To highlight methodologies popular in the last five years.
  • To identify information-preserving techniques.

Main Methods:

  • Literature review and classification of shape analysis algorithms.
  • Categorization based on analysis scope (boundary/area) and descriptive approach (scalar/structural).
  • Focus on algorithms developed or popularized within the last five years.

Main Results:

  • A structured classification of shape analysis algorithms is presented.
  • Popular and information-preserving methods from recent years are identified.
  • The review covers algorithms examining image boundaries and entire areas.

Conclusions:

  • The classification provides a framework for understanding the landscape of shape analysis.
  • Information-preserving methods are key for robust shape analysis.
  • Future research can build upon this classification to develop advanced algorithms.