Recap. DoF Constraint Solver. translation. affine. homography. 3D rotation

Transcription

1 Image Blending

2 Recap DoF Constraint Solver translation affine homography 3D rotation

3 Recap DoF Constraint Solver translation 2 affine homography 3D rotation

4 Recap DoF Constraint Solver translation 2 affine 6 homography 8 3D rotation 3

5 Recap DoF Constraint Solver translation 2 At=b affine 6 homography 8 3D rotation 3

6 Recap DoF Constraint Solver translation 2 At=b affine 6 At=b homography 8 At=0 3D rotation 3 Rpi=qi

7 Recap DoF Constraint Solver translation 2 At=b (A A) b -1 affine 6 At=b (A A) b -1 homography 8 At=0 3D rotation 3 Rpi=qi Eigen-vector of (A A) SVD (singular value decomposition)

8 Image Blending We ve aligned the images now what?

9 Image Blending Want to seamlessly blend them together

10 Image Blending [ Marc Pollefeys ]

11 An Image From NASA [ Alyosya Efros ]

12 Image Feathering 12

13 Feathering =

14 Effect of window size 1 left 1 0 right 0 14

15 Effect of window size

16 Good window size 1 0 Optimal window: smooth but not ghosted Doesn t always work... 16

17 Image feathering What if you re blending more than two images?

18 Image feathering What if you have more than two images? Generate weight map for each image typically want large weight at center, small weight at edge Each output pixel is a weighted average of inputs be sure to divide by sum of weights at the end

19 Alpha Blending I 3 p I 1 I 2 Optional: see Blinn (CGA, 1994) for details: isnumber=7531&prod=jnl&arnumber=310740&arst=83&ared =87&arAuthor=Blinn%2C+J.F. Encoding blend weights: I(x,y) = (αr, αg, αb, α) color at p = Implement this in two steps: 1. accumulate: add up the (α premultiplied) RGBα values at each pixel 2. normalize: divide each pixel s accumulated RGB by its α value Q: what if α = 0?

20 More advanced blending schemes A quick survey...

21 Pyramid blending Create a Laplacian pyramid, blend each level Burt, P. J. and Adelson, E. H., A multiresolution spline with applications to image mosaics, ACM Transactions on Graphics, 42(4), October 1983,

22 The Laplacian Pyramid Gaussian Pyramid Laplacian Pyramid subsample - = subsample subsample - = - =

23 Laplacian level 4 Laplacian level 2 Laplacian level 0 Richard Szeliski Image Stitching 52 left pyramid right pyramid blended pyramid

24 The Laplacian Pyramid Blended Gaussian Pyramid Blended Laplacian Pyramid expand expand = + expand = + = +

25 Laplacian image blend 1. Compute Laplacian pyramid 2. Compute Gaussian pyramid on weight image 3. Blend Laplacians using Gaussian blurred weights 4. Reconstruct the final image 25

26 Examples Mix an eye and a hand...

27 Examples Mix an eye and a hand... [ david dmartin, Boston College ]

28 Examples Mix an eye and a hand... [ Chris Cameron, CMU ]

29 Simplification: Two-band Blending Brown & Lowe, 2003 Only use two bands: high freq. and low freq. Blends low freq. smoothly Blend high freq. with no smoothing: use binary mask

30 2-band Blending Low frequency (λ > 2 pixels) High frequency (λ < 2 pixels)

31 Linear Blending

32 2-band Blending

33 Gradient-domain blending Blend the gradients of the two images, then integrate For more info: Perez et al, SIGGRAPH 2003 Also called Poisson blending

34 De-Ghosting

35 Local alignment (deghosting) Use local optic flow to compensate for small motions [Shum & Szeliski, ICCV 98] 35

36 Local alignment (deghosting) Use local optic flow to compensate for radial distortion [Shum & Szeliski, ICCV 98] 36

37 Region-based de-ghosting Select only one image in regions-of-difference using weighted vertex cover [Uyttendaele et al., CVPR 01] 37

38 Cutout-based de-ghosting Select only one image per output pixel, using spatial continuity Blend across seams using gradient continuity ( Poisson blending ) [Agarwala et al., SG 2004] 38

39 Cutout-based compositing Photomontage [Agarwala et al., SG 2004] Interactively blend different images: group portraits 39

40 Photomontage [Agarwala et al., SG 2004]

41 Cutout-based compositing Photomontage [Agarwala et al., SG 2004] Interactively blend different images: focus settings 41

42 Cutout-based compositing Photomontage [Agarwala et al., SG 2004] Interactively blend different images: people s faces 42

43 More stitching possibilities Video stitching High dynamic range image stitching see demo Flash + Non-Flash Video-based rendering Computational Photography! 43