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Optimal edge-based shape detection.

Hankyu Moon1, Rama Chellappa, Azriel Rosenfeld

  • 1Center for Automation Research, University of Maryland, College Park, MD 20742-3275, USA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 6, 2008
PubMed
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This study introduces a novel method for accurate two-dimensional (2-D) shape detection using an optimal step edge operator. The approach extends edge detection to global contour detection for improved shape recognition.

Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Accurate detection of two-dimensional (2-D) shapes is crucial in various image analysis tasks.
  • Existing methods often struggle with noise and complex shape geometries.
  • Modeling shape boundaries as step functions provides a robust foundation for detection.

Purpose of the Study:

  • To develop an accurate and robust method for 2-D shape detection.
  • To extend 1-D edge detection principles to global contour detection.
  • To analyze and predict the performance of the proposed shape detection operator.

Main Methods:

  • Derived a 1-D optimal step edge operator (Derivative of Double Exponential - DODE) minimizing noise and mean squared error.
  • Extended the DODE filter along shape contours for 2-D shape detection.

Related Experiment Videos

  • Accumulated filter responses at the centroid to estimate shape presence likelihood.

    • Main Results:

    • The DODE operator effectively models shape boundaries as step functions.
    • The proposed method extends pixel-level edge detection to global contour detection.
    • Statistical properties of the response were computed, enabling performance prediction and parameter adjustment.

    Conclusions:

    • The DODE-based shape detection approach offers a systematic tool for edge-based shape analysis.
    • The method demonstrates predictable localization and detection performance under general assumptions.
    • Successfully applied to vehicle detection, facial feature detection, and contour tracking.