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Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
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Symbol recognition with kernel density matching.

Wan Zhang1, Liu Wenyin, Kun Zhang

  • 1Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong. wanzhang@cityu.edu.hk

IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 17, 2006
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for graphic symbol similarity assessment using 2D kernel densities and Kullback-Leibler divergence. The approach accurately determines symbol orientation, demonstrating excellent performance across diverse applications.

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Area of Science:

  • Computer Vision
  • Pattern Recognition
  • Image Analysis

Background:

  • Assessing similarity between graphic symbols is crucial for tasks like image retrieval and pattern matching.
  • Existing methods may struggle with variations in symbol orientation and complex density distributions.

Purpose of the Study:

  • To develop a robust and accurate method for graphic symbol similarity assessment.
  • To effectively handle variations in symbol orientation and representation.

Main Methods:

  • Representing graphic symbols as two-dimensional (2D) kernel densities.
  • Utilizing Kullback-Leibler (KL) divergence to quantify similarity between symbol representations.
  • Employing gradient-based angle searching and independent component analysis (ICA) for symbol orientation determination.

Main Results:

  • The proposed method achieves outstanding performance in graphic symbol similarity assessment.
  • The approach demonstrates effectiveness across various experimental conditions and symbol types.
  • Accurate symbol orientation identification contributes to improved similarity measures.

Conclusions:

  • The novel approach using 2D kernel densities and KL divergence offers a powerful tool for graphic symbol analysis.
  • The integration of orientation detection enhances the robustness of similarity assessment.
  • This method shows significant potential for applications requiring precise graphic symbol comparison.