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Related Concept Videos

Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
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Rapidly Varying Flow01:24

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Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro
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A nonconservative flow field for robust variational image segmentation.

Pratim Ghosh1, Luca Bertelli, Baris Sumengen

  • 1Vision and ResearchLaboratory, Department of Electrical and Computer Engineering, University of California Santa Barbara, Santa Barbara, CA, 93106, USA. pratim@ece.ucsb.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 20, 2010
PubMed
Summary
This summary is machine-generated.

This study presents a novel image segmentation technique using edge flow vectors for improved object boundary localization, especially for complex shapes. The method proves competitive against state-of-the-art approaches in experiments.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Mathematics

Background:

  • Accurate image segmentation is crucial for numerous applications.
  • Existing methods struggle with objects exhibiting concavities.
  • Robust boundary localization remains a challenge in image analysis.

Purpose of the Study:

  • To introduce a novel variational image segmentation method.
  • To enhance object boundary localization, particularly for complex shapes.
  • To demonstrate the method's competitiveness against state-of-the-art techniques.

Main Methods:

  • Utilizing a variational formulation with edge flow vectors.
  • Leveraging the nonconservative nature of the flow field for segmentation.
  • Developing and applying a multiscale version of the segmentation method.

Main Results:

  • The proposed method effectively segments objects, including those with concavities.
  • The multiscale approach significantly improves the localization of object boundaries.
  • Experimental results on synthetic and natural images show competitive performance.

Conclusions:

  • The edge flow vector-based variational method offers a robust approach to image segmentation.
  • The nonconservative flow field is key to handling complex object geometries.
  • The multiscale extension enhances precision in boundary detection, making it a competitive technique.