Data-driven methods: Texture 2 (Sz 10.5)

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Presentation transcript:

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

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

Texture Transfer Constraint Texture sample

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”

+ =

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

Image Analogies A A’ B B’

Blur Filter

Edge Filter

Artistic Filters A A’ B B’

Colorization

Texture-by-numbers A A’ B B’

Super-resolution Input A A’

Super-resolution Result B B’

Fill Order In what order should we fill the pixels?

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.

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

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

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

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

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