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Contourlet Transforms For Feature Detection

Contourlet Transforms For Feature Detection. Wei-shi Tsai April 29th, 2008. Feature Detection. Focus will be on edge detection Gradient operators (Sobel, Roberts) Laplacian operators LoG (Laplacian of Gaussian) DoG (Difference of Gaussians) Canny method Anisotropic diffusion.

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Contourlet Transforms For Feature Detection

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  1. Contourlet Transforms For Feature Detection Wei-shi Tsai April 29th, 2008

  2. Feature Detection • Focus will be on edge detection • Gradient operators (Sobel, Roberts) • Laplacian operators • LoG (Laplacian of Gaussian) • DoG (Difference of Gaussians) • Canny method • Anisotropic diffusion

  3. Contourlets (Do and Vetterli, 2005) • Captures smooth contours and edges at any orientation • Filters noise • Derived directly from discrete domain instead of extending from continuous domain • Can be implemented using filter banks

  4. Contourlet filter bank The transform decouples the multiscale and the directional decompositions.

  5. Test Pattern Image – Scale 1

  6. Test Pattern Image – Scale 2

  7. Peppers Image – Scale 1

  8. Peppers Image – Scale 2

  9. Generic Girl Image – Scale 1

  10. Generic Girl Image – Scale 2

  11. Tiffany Image – Scale 1

  12. Tiffany Image – Scale 2

  13. Elaine Image – Scale 1

  14. Elaine Image – Scale 2

  15. Lena Image – Scale 1

  16. Lena Image – Scale 2

  17. Conclusions • Contourlet transforms can be used for edge detection • Results can vary based on the type of image • Evaluation is only useful given what the feature extracted is to be used

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