1	Announcements Project 4 questions? Review thisThursday Bring your questions! Final Exam 10:30-12:20pm, Thursday, Mar. 20 Evaluations today at the end of class
2	Texture Synthesis
3	Texture Today’s Reading Alexei A. Efros and Thomas K. Leung, “Texture Synthesis by Non-parametric Sampling,” Proc. International Conference on Computer Vision (ICCV), 1999. http://www.cs.berkeley.edu/~efros/research/NPS/efros-iccv99.pdf
4	Modeling Texture What is texture? How can we model it?
5	Markov Chains Markov Chain a sequence of random variables is the state of the model at time t Markov assumption: each state is dependent only on the previous one dependency given by a conditional probability: The above is actually a first-order Markov chain An N’th-order Markov chain:
6	Markov Chain Example: Text “A dog is a man’s best friend. It’s a dog eat dog world out there.”
7	Text synthesis Create plausible looking poetry, love letters, term papers, etc. Most basic algorithm Build probability histogram find all blocks of N consecutive words/letters in training documents compute probability of occurance Given words compute by sampling from Example on board...
8	[Scientific American, June 1989, Dewdney] “I Spent an Interesting Evening Recently with a Grain of Salt” - Mark V. Shaney (computer-generated contributor to UseNet News group called net.singles) You can try it online here: http://www.yisongyue.com/shaney/
9	Modeling Texture What is texture? An image obeying some statistical properties Similar structures repeated over and over again Often has some degree of randomness
10	Markov Random Field
11	Texture Synthesis [Efros & Leung, ICCV 99] Can apply 2D version of text synthesis
12	Synthesizing One Pixel What is ? Find all the windows in the image that match the neighborhood consider only pixels in the neighborhood that are already filled in To synthesize x pick one matching window at random assign x to be the center pixel of that window
13	Really Synthesizing One Pixel
14	Growing Texture
15	Window Size Controls Regularity
16	More Synthesis Results
17	More Results
18	Failure Cases
19	Image-Based Text Synthesis
20	Extrapolation
21	Speed Given: image of k² pixels Output: image of n² pixels how many window comparisons does this algorithm require?
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25	Fill Order In what order should we fill the pixels?
26	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
27	Exemplar-based Inpainting demo
28	More on Image Inpainting Can also be formulated as image diffusion Idea of propagating along lines comes from Bertalmío, Sapiro, Caselles, and Ballester, “Image Inpainting,” Proc. SIGGRAPH 2000.
29	Image Inpainting
30	Image Inpainting
31	Image Inpainting
32	Texture Transfer [Efros & Freeman 2001]
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36	Combining two images
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38	Graph cut setup
39	Graph cut texture synthesis: Video
40	Image Analogies (Hertzmann ’01)
41	Artistic Filters
42	Texture-by-numbers
43	Colorization
44	References Efros and Leung, “Texture Synthesis by Non-parametric Sampling,” Proc. ICCV, 1999. Efros and Freeman, “Image Quilting for Texture Synthesis and Transfer,” Proc. SIGGRAPH 2001. Bertalmío, Sapiro, Caselles, and Ballester, “Image Inpainting,” Proc. SIGGRAPH 2000. Criminisi, Perez, and Toyama. “Object Removal by Exemplar-based Inpainting,” Proc. CVPR, 2003. Kwatra, Schödl, Essa, Turk, and Bobick, “Graphcut Textures: Image and Video Synthesis Using Graph Cuts,” Proc. SIGGRAPH 2003. Hertzmann, Jacobs, Oliver, Curless, and Salesin, “Image Analogies,” Proc. SIGGRAPH 2001. Bhat, Seitz, Hodgins, Khosla, “Flow-Based Video Synthesis and Editing,” Proc. SIGGRAPH 2004.