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A framework for image segmentation using shape models and kernel space shape priors.

Samuel Dambreville1, Yogesh Rathi, Allen Tannenbaum

  • 1School of Electrical and Computer Engineering. Georgia Institute of Technology, Atlanta, GA 30332-0250, USA. samuel.dambreville@bme.gatech.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|June 21, 2008
PubMed
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This study introduces a novel image segmentation method using kernel PCA (KPCA) for robust shape prior integration. The technique enhances segmentation accuracy, effectively handling noise and occlusions for improved object separation.

Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Image segmentation using only visual data is often impaired by noise, clutter, and occlusions.
  • Geometric Active Contour (GAC) models can be improved by incorporating prior shape knowledge.

Purpose of the Study:

  • To propose a novel image segmentation method that integrates image data with prior shape information.
  • To enhance the robustness of segmentation against image degradations like noise and occlusions.

Main Methods:

  • A level-set framework is employed for image segmentation.
  • Kernel PCA (KPCA) is utilized to robustly encode prior shape knowledge, outperforming linear PCA.
  • Shape knowledge and image information are represented within energy functionals.

Related Experiment Videos

Main Results:

  • The proposed KPCA-based method demonstrates superior performance compared to linear PCA.
  • The segmentation technique effectively handles multiple shape types simultaneously.
  • Robustness against noise, occlusions, and smearing is significantly improved.

Conclusions:

  • Combining image information with KPCA-driven shape priors offers a powerful approach to image segmentation.
  • The method provides a robust and accurate solution for object separation in challenging image conditions.