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Adaptive snakes using the EM algorithm.

Jacinto C Nascimento1, Jorge S Marques

  • 1Instituto Superior Técnico/Instituto de Sistemas e Robótica, Lisboa, Portugal. jan@isr.ist.utl.pt

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|November 11, 2005
PubMed
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This study introduces a novel algorithm for image analysis, improving deformable models by classifying edge points into valid and invalid strokes. This enhances object contour detection, even in cluttered images.

Area of Science:

  • Computer Vision
  • Image Analysis
  • Computational Geometry

Background:

  • Deformable models, such as active contours (snakes), struggle with accuracy in image analysis due to spurious edge points.
  • Non-object edge points attract deformable models, hindering convergence to the true object boundary.

Purpose of the Study:

  • To develop a robust algorithm that overcomes the limitations of traditional deformable models in object contour detection.
  • To improve the accuracy and reliability of active contour models in complex image environments.

Main Methods:

  • A new algorithm associates detected edge points into 'strokes'.
  • Each stroke is classified as valid (inlier) or invalid (outlier) with an associated confidence degree.
  • The Expectation-Maximization (EM) algorithm is employed to update confidence degrees and estimate the object contour, effectively creating an adaptive potential function.

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Main Results:

  • The algorithm demonstrates remarkable robustness in the presence of image clutter.
  • Valid strokes are assigned high confidence, while invalid strokes' confidence diminishes during optimization.
  • Experimental results validate the improved performance of the proposed method over traditional approaches.

Conclusions:

  • The proposed stroke-based classification and EM algorithm significantly enhance the performance of deformable models for object contour detection.
  • This adaptive approach provides a more reliable method for image analysis, particularly in challenging, cluttered scenes.