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Automated Joint Space Detection Improves Bone Segmentation Accuracy
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Attributed string matching with merging for shape recognition.

W H Tsai1, S S Yu

  • 1Department of Information Science and the Microelectronics and Information Science and Technology Research Center, National Chiao Tung University, Hsinchu, Taiwan 300, Republic of.

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

This study introduces an enhanced attributed string matching method with merging for shape recognition. This approach effectively identifies distorted shapes by matching boundary primitives, improving upon traditional methods.

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Area of Science:

  • Computer Vision
  • Pattern Recognition
  • Image Analysis

Background:

  • Conventional symbolic string matching for shape recognition has limitations, particularly with insertions, deletions, and changes.
  • Existing methods struggle with the inherent variability and distortions found in real-world shapes.

Purpose of the Study:

  • To propose a novel structural approach for shape recognition using attributed string matching with a merging operation.
  • To overcome the limitations of conventional string matching techniques in handling shape distortions.

Main Methods:

  • Representing shapes as attributed strings, where primitives (line segments) have numerical attributes (length, direction).
  • Introducing a 'merge' edit operation to combine consecutive primitives for flexible matching.
  • Applying attributed string matching with merging to compare and recognize shapes.

Main Results:

  • The proposed method demonstrates effectiveness in recognizing distorted shapes.
  • Experimental results validate the feasibility of attributed string matching with merging for general shape recognition.

Conclusions:

  • Attributed string matching with merging offers a robust solution for shape recognition, especially for complex and distorted forms.
  • The approach shows promise for various applications in pattern recognition and computer vision.