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Related Experiment Videos

Multiple contour extraction from graylevel images using an artificial neural network.

Y V Venkatesh1, S Kumar Raja, N Ramya

  • 1Department of Electrical and Computer Engineering, Faculty of Engineering, National University of Singapore, Singapore 117576. eleyvv@nus.edu.sg

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|April 4, 2006
PubMed
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We introduce the batch-SOM (BSOM), a novel self-organizing map (SOM) approach for active contour modeling (ACM). This method effectively extracts object contours from images, outperforming existing techniques.

Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Active contour modeling (ACM) is crucial for image segmentation.
  • Existing snake-based ACMs rely on explicit energy functionals, which can be sensitive to image noise and weak edges.
  • Self-organizing map (SOM)-based ACMs offer an alternative but may lack robustness in certain scenarios.

Purpose of the Study:

  • To propose a novel SOM-based active contour model, the batch-SOM (BSOM).
  • To integrate the strengths of SOM and snake-based ACMs for robust contour extraction.
  • To extend the BSOM for segmenting multiple objects within an image.

Main Methods:

  • The batch-SOM (BSOM) utilizes feature points from edge maps to guide contour evolution.
  • It incorporates local gradient and intensity variations to prevent contour leakage, even with imperfect feature points.

Related Experiment Videos

  • Unlike snake models, BSOM avoids explicit energy functional minimization for contour control.
  • An extended BSOM version handles multi-object segmentation by splitting contours.

    • Main Results:

    • The BSOM demonstrates superior performance in extracting contours from synthetic and real-world images.
    • It effectively handles images with single and multiple objects, including those with convex and nonconvex boundaries.
    • The extended BSOM successfully segments multiple objects by adaptive contour splitting.

    Conclusions:

    • The BSOM offers a robust and effective alternative to traditional snake-based ACMs.
    • Its ability to integrate feature points with local image properties enhances contour extraction accuracy.
    • The extended BSOM provides a viable solution for multi-object segmentation tasks.