1. Data-driven methods: Texture 2 (Sz 10.5)
Cs129 Computational Photography
James Hays, Brown, Spring 2011
Many slides from Alexei Efros

2. Application: Texture Transfer
Try to explain one object with bits and pieces of another object: + =

3. Texture Transfer
Constraint
Texture sample

4. Texture Transfer
Take the texture from one image and “paint” it onto another object
Same as texture synthesis, except an additional constraint:
Consistency of texture
Similarity to the image being “explained”

5. + =

7. Image Analogies Aaron Hertzmann1,2 Chuck Jacobs2 Nuria Oliver2
Brian Curless3
David Salesin2,3
1New York University
2Microsoft Research
3University of Washington

8. Image Analogies A A’ B B’

10. Blur Filter

11. Edge Filter

12. Artistic Filters A A’ B B’

13. Colorization

14. Texture-by-numbers A A’ B B’

15. Super-resolution InputA A’

16. Super-resolution ResultB B’

17. Fill Order
In what order should we fill the pixels?

18. Fill Order In what order should we fill the pixels?
choose pixels that have more neighbors filled
choose pixels that are continuations of lines/curves/edges
Criminisi, Perez, and Toyama. “Object Removal by Exemplar-based Inpainting,” Proc. CVPR, 2003.

19. Criminisi, Perez, and Toyama
Criminisi, Perez, and Toyama. “Object Removal by Exemplar-based Inpainting,” Proc. CVPR, 2003.

20. Criminisi, Perez, and Toyama
Criminisi, Perez, and Toyama. “Object Removal by Exemplar-based Inpainting,” Proc. CVPR, 2003.

21. Confidence Term Data Term
Criminisi, Perez, and Toyama. “Object Removal by Exemplar-based Inpainting,” Proc. CVPR, 2003.

22. Criminisi, Perez, and Toyama
Criminisi, Perez, and Toyama. “Object Removal by Exemplar-based Inpainting,” Proc. CVPR, 2003.

23. Graph cut vs Dynamic Programming
What’s the advantage of a graph cut over dynamic programming here?