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Technical Report.pdf

Fundana-Heyden-Gosch-Schnoerr-2008.pdf
Preview of Technical Report
🔗 Source: hci.iwr.uni-heidelberg.de
📊 Size: 414 KB
📄 Pages: 4 pages
⬇️ Downloads: 54

Summary

A novel prior-based variational object segmentation method is proposed, which unifies image segmentation and denoising in a global minimization framework. The method, called continuous graph cuts, convexifies the energy functional of the Chan-Vese method to find a global minimizer. An additional shape constraint is added to the convex energy functional to segment an object using prior information. The energy functional can be relaxed to optimization over arbitrary functions, followed by thresholding at an arbitrary level between 0 and 1. Experimental results demonstrate the performance and robustness of the method in segmenting objects in real images.

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Description

A novel prior-based variational object segmentation method is proposed, which unifies image segmentation and denoising in a global minimization framework.

Technical Information

  • File Format: PDF
  • File Size: 414 KB
  • Pages: 4
  • Language: EN
  • Total Downloads: 54
  • Last Updated: 7 days ago

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