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Related Experiment Videos

Detection of composite edges.

S Ghosal1, R Mehrotra

  • 1Comput. Math. Group, Colorado Univ., Denver, CO.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1994
PubMed
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This study introduces a novel parametric model for high-precision composite edge detection using Zernike moments. The method effectively identifies generalized step and pulse/staircase edges in images, demonstrating robustness against noise.

Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Accurate edge detection is crucial for image analysis and computer vision tasks.
  • Existing methods struggle with complex edge types like generalized step, pulse, and staircase edges.
  • Parametric modeling offers a robust framework for detailed edge characterization.

Purpose of the Study:

  • To propose a new parametric model-based approach for high-precision composite edge detection.
  • To develop and validate orthogonal Zernike moment-based operators for detecting generalized step and pulse/staircase edges.
  • To assess the robustness and efficacy of the proposed method against noise and compare it with existing techniques.

Main Methods:

  • A parametric model for 2-D generalized step edges was defined using five parameters (gradients, distance, orientation, step size).

Related Experiment Videos

  • A parametric model for 2-D pulse/staircase edges was defined using two steps and edge orientation.
  • Orthogonal Zernike moment-based operators (two complex, two real) were designed to extract model parameters.
  • Noise analysis was conducted to evaluate operator robustness.

    • Main Results:

    • The proposed Zernike moment-based operators successfully determined parameters for both generalized step and pulse/staircase edge models.
    • The technique demonstrated high precision in detecting composite edges in both intensity and range images.
    • Experimental results confirmed the method's robustness against noise and superior performance compared to geometric moment-based and Canny edge detectors.

    Conclusions:

    • The developed parametric model-based approach using orthogonal Zernike moments provides a robust and precise method for composite edge detection.
    • This technique effectively distinguishes between generalized step, pulse, and staircase edges, outperforming traditional methods.
    • The findings have significant implications for advanced image analysis applications requiring accurate edge localization.