Seamless Image Stitching in the Gradient Domain

1. Seamless Image Stitching in the Gradient Domain Levin, Zomet, Peleg and Weiss ECCV 2004

2. Image Stitching Overlap Regions

3. New Approach : GIST Goal: Minimize the dissimilarity between the derivatives of the images Gradient-domain Image STitching (GIST) Take two aligned input images, overlap them, minimize the distance between their derivatives.

4. GIST I1 I2

5. GIST Fast numerical algorithm • Initialize the solution image I • Iterate until convergence • For all x,y in the image, update I(x,y) to be:

6. Problems • Initialize the image to what? • Black or white? • Optimal seam? • Feathering? • Each pixel in the image is determined from the median of four other pixels and derivatives. This will result in blurring. • Why go over the entire image and not just the overlapping area?

7. Emailed the Author • “When preparing the journal version, we found that on many examplesit has convergence problems, and therefore proposed an alternativealgorithm, based on iterative reweighted least squares.” • Three MATLAB routines • How did it fit in with paper implementation

8. Problems continued • “This algorithm is slow, but at least it converges.” • Too complicated. Too slow.

9. Previous approaches • Feathering – handles global intensity differences, causes ghosting. • Idea – only feather where there is a small difference in intensity values.

10. Feathering

11. Selective Feathering (black leftover)

12. Selective Feathering (white leftover)

13. Selective Feathering (closest overlap)

14. Edges • Look at the gradient of the image • If there is an edge in the new image but not in either of the two overlapping images, blend.

15. Selective Blending

16. Feathering

17. Selective Blending

18. Selective Feathering (closest overlap)

19. Feathering

20. Optimal Cut

21. Other Results

22. Other Results

23. GIST vs ME

24. GIST vs ME 2

25. That’s it