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Related Experiment Videos

A faster converging snake algorithm to locate object boundaries.

Mustafa Sakalli1, Kin-Man Lam, Hong Yan

  • 1School of Electrical and Information Engineering, University of Sydney, Sydney NSW 2006, Australia. sakalli@ee.usyd.edu.au

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|May 5, 2006
PubMed
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A novel contour search algorithm offers faster convergence than existing methods by using alternate skipping and adjusted step sizes. This approach minimizes pseudo-local minima, improving object contour detection and face profile extraction.

Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Geometry

Background:

  • Object contour detection is crucial in image analysis.
  • Existing algorithms like Greedy Snake Algorithm (GSA) and Fast Greedy Snake (FGSA) have limitations in convergence speed and susceptibility to pseudo-local minima.

Purpose of the Study:

  • To introduce a new contour search algorithm with enhanced convergence speed.
  • To improve the accuracy of object contour detection by reducing entrapment in pseudo-local minima.
  • To apply the algorithm for hierarchical face profile extraction.

Main Methods:

  • A novel contour search algorithm utilizing alternate skipping between even and odd nodes (snaxels) with varying step sizes.
  • A coarse-to-fine iterative approach to refine convergence.

Related Experiment Videos

  • Comparison with the Fast Greedy Snake (FGSA) algorithm.
  • Integration with subband decomposition for hierarchical face profile extraction.

    • Main Results:

    • The proposed algorithm demonstrates faster convergence to object contours compared to GSA and FGSA.
    • Adjustable step sizes reduce the likelihood of the snake being trapped in pseudo-local minima.
    • The algorithm effectively extracts face profiles hierarchically when combined with subband decomposition.

    Conclusions:

    • The new contour search algorithm offers a significant improvement in speed and accuracy over existing methods.
    • The adaptive step size strategy is key to overcoming limitations of previous algorithms.
    • The algorithm shows promise for applications like hierarchical face profile analysis.