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Object matching algorithms using robust Hausdorff distance measures.

D G Sim1, O K Kwon, R H Park

  • 1Department of Electronic Engineering, Sogang University, Seoul 100-611, Korea.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 12, 2008
PubMed
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This study introduces robust Hausdorff distance (HD) measures for object matching, improving efficiency over conventional methods. Simulations show enhanced performance in comparing synthetic and real images.

Area of Science:

  • Computer Vision
  • Image Analysis
  • Pattern Recognition

Background:

  • The Hausdorff distance (HD) is a standard metric for comparing sets, widely used in object matching and image analysis.
  • Conventional HD measures can be sensitive to outliers, potentially affecting matching accuracy in real-world applications.

Purpose of the Study:

  • To analyze the performance of conventional Hausdorff distance measures.
  • To propose and evaluate two novel, robust Hausdorff distance measures based on m-estimation and least trimmed squares (LTS).
  • To compare the efficiency and matching performance of the proposed measures against conventional ones.

Main Methods:

  • Analysis of conventional Hausdorff distance algorithms.
  • Development of two new robust HD measures utilizing m-estimation and LTS principles.

Related Experiment Videos

  • Computer simulations involving synthetic and real image datasets for comparative analysis.

    • Main Results:

    • The proposed robust HD measures demonstrate improved efficiency compared to conventional methods.
    • Comparative simulations indicate superior matching performance of the novel measures.
    • The LTS-based measure shows particular robustness against outliers.

    Conclusions:

    • The novel m-estimation and LTS-based Hausdorff distance measures offer a more robust and efficient alternative for object matching.
    • These advanced HD metrics enhance image analysis accuracy, especially in the presence of noisy or aberrant data.
    • The findings suggest practical benefits for applications requiring precise object recognition and comparison.