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A new algorithm for image edge extraction using a statistical classifier approach.

A Kundu1, S K Mitra

  • 1Department of Electrical Engineering, State University of New York, Amherst Campus, Buffalo, NY 14260.

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

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A novel algorithm extracts edges from natural images by treating edge detection as a statistical classification problem, effectively handling noise. This new edge operator offers user-adjustable parameters and is compared to Marr-Hildreth

Area of Science:

  • Computer Vision
  • Image Processing
  • Statistical Modeling

Background:

  • Edge detection is crucial for image analysis.
  • Existing methods like Marr-Hildreth have limitations, especially with noisy images.

Purpose of the Study:

  • To introduce a new, robust algorithm for edge extraction from natural images.
  • To present edge extraction as a statistical classifier problem.

Main Methods:

  • Developed a novel algorithm based on a simple image model.
  • Formulated edge extraction as a statistical classifier problem.
  • Provided a step-by-step mathematical derivation.

Main Results:

  • The algorithm successfully extracts and detects edges in natural images, even with significant noise.

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  • Demonstrated the algorithm's flexibility through adjustable parameters.
  • Compared the proposed operator against the Marr-Hildreth edge operator.

    • Conclusions:

    • The new algorithm offers a flexible and noise-robust approach to edge extraction.
    • The statistical classifier framework provides a powerful method for image analysis tasks.