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Related Experiment Video

Updated: Jul 16, 2026

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
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Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Published on: February 27, 2016

Global regularizing flows with topology preservation for active contours and polygons.

Ganesh Sundaramoorthi1, Anthony Yezzi

  • 1School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. ganeshs@ece.gatech.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|March 16, 2007
PubMed
Summary

This study introduces a novel geometric flow for active contours and polygons, ensuring topology preservation during image segmentation. This method gracefully prevents topological changes, enhancing segmentation accuracy and stability.

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Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
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Published on: February 3, 2014

Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Geometry

Background:

  • Active contour and active polygon models are widely used for image segmentation.
  • Maintaining object topology during segmentation is crucial for many applications.
  • Existing methods may struggle with preserving topology during complex geometric evolutions.

Purpose of the Study:

  • To develop a novel geometric flow for active contours and polygons.
  • To ensure the preservation of the initial topology throughout the segmentation process.
  • To provide a more stable and accurate segmentation by preventing premature topological changes.

Main Methods:

  • Construction of a novel geometric flow integrated with image-based active contour/polygon evolution.
  • Utilizing electrostatic principles to define an energy-based gradient flow.
  • Implementing a gradual adjustment mechanism to prevent topology changes proactively.

Main Results:

  • The proposed geometric flow successfully preserves the topology of the initial contour/polygon.
  • The flow acts as a global regularity term, similar to curvature flow, enhancing contour smoothness.
  • Gradual adjustments and global regularization prevent geometrical inaccuracies common in discrete methods.

Conclusions:

  • The novel topology-preserving geometric flow offers a robust solution for image segmentation tasks requiring topological stability.
  • This method provides a graceful and continuous approach to topology preservation, outperforming simple discrete schemes.
  • The electrostatic energy-based flow ensures that contour evolution is globally regularized and less prone to topological errors.