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Krivljenje slike - warping

Krivljenje slike - warping. Princip 2D krivljenja. Demo. Krivljenje ( Warping ). A warp is a 2-D geometric transformation and generates a distorted image when it is applied to an image. Warping an image means : apply a given deformation to it.

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Krivljenje slike - warping

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  1. Krivljenje slike - warping

  2. Princip 2D krivljenja Demo

  3. Krivljenje (Warping) • A warp is a 2-D geometric transformation and generates a distorted image when it is applied to an image. • Warping an image means : apply a given deformation to it. • Two ways to warp an image:- Forward mapping.  Reverse mapping.

  4. Krivljenje (Warping) warp Destination image Source image

  5. Preslikava (mapping)

  6. Primer preslikave

  7. Primer preslikave

  8. Primer preslikave

  9. Primer krivljenja

  10. Other Mappings

  11. Image Warping Implementation I

  12. Forward Mapping

  13. Forward Mapping

  14. Forward Mapping

  15. Image Warping Implementation II

  16. Reverse Mapping

  17. Forward and Reverse Mapping • Forward  Some pixels in the destination might not get painted, and would have to be interpolated. • Reverse  Every pixel in the destination image gets set to something appropriate.

  18. Resampling • Evaluate source image at arbitrary (u, v) • (u, v) does not usually have integer coordinates • Some kinds of resampling • Point resampling • Triangle filter • Gaussian filter Source Image Destination Image

  19. Point Sampling • Take value at closest pixel int iu = trunc(u + 0.5); int iv = trunc(v + 0.5); dst(x, y) = src(iu, iv); • Simple, but causes aliasing

  20. Triangle Filter • Convolve with triangle filter

  21. Triangle Filter • Bilinearly interpolate four closest pixels a = linear interpolation of src(u1, v2) and src(u2, v2) b = linear interpolation of src(u1, v1) and src(u2, v1) dst(x, y) = linear interpolation of ‘a’ and ‘b’

  22. Gaussian Filter • Convolve with Gaussian filter Width of Gaussian kernel affects bluriness

  23. Filtering Method Comparison • Trade-offs • Aliasing versus blurring • Computation speed

  24. Image Warping Implementation

  25. Image Warping Implementation

  26. Example: Scale

  27. Example: Rotate

  28. Example: Swirl

  29. Image Warping: Summary

  30. Forward and Reverse Mapping • In either case, the problem is to determine the way in which the pixels in one image should be mapped to the pixels in the other image. • So, we need to specify how each pixel moves between the two images. • This could be done by specifying the mapping for a few important pixels.

  31. Two Dimensional Object Warping

  32. Two Dimensional Object Warping • The shape modification can also be performed on a vertex-basis instead of on a space-basis. • A displacement for a seed vertex can be specified by the user and this displacement can be propagated to nearby vertices. • The displacement can be attenuated as a function of the distance that the vertex to be displaced is away from the seed vertex.

  33. Two Dimensional Object Warping • The distance function can be chosen to trade-off quality of results and computational complexity. • Minimum number of edges connecting the vertex to be displaced from the seed vertex. • Minimum distance traveled over the surface of the object to get from the vertex to be displaced to the seed vertex. • Power functions to control the amount of attenuation. • The user could select the maximum distance at which the displacement would have an affect.

  34. Texture mapping

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