1	Announcements Guest lecture today: Aseem Agarwala Final project out today you and your partner must submit a proposal by this Friday 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
2	Modeling Texture What is texture? How can we model it?
3	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:
4	Markov Chain Example: Text “A dog is a man’s best friend. It’s a dog eat dog world out there.”
5	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...
6	[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)
7	Modeling Texture What is texture? An image obeying some statistical properties Similar structures repeated over and over again Often has some degree of randomness
8	Markov Random Field
9	Texture Synthesis [Efros & Leung, ICCV 99] Can apply 2D version of text synthesis
10	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
11	Really Synthesizing One Pixel
12	Growing Texture
13	Window Size Controls Regularity
14	More Synthesis Results
15	More Results
16	Failure Cases
17	Image-Based Text Synthesis
18	Speed
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23	Texture Transfer
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27	Issues Imposes artificial grid of overlapping blocks on synthesized image, and greedily chooses blocks in left-right, top-bottom order Dynamic programming limits applicability to related problems. Solution: use graph cuts instead Let’s explore two examples, first.
28	Combining two images
29	Graph cut setup
30	Spatio-temporal texture synthesis
31	Graphcut textures (Kwatra ’03)
32	Progressive Refinement
33	Comparison
34	Image Analogies (Hertzmann ’01)
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36	Artistic Filters
37	Texture-by-numbers
38	Other applications of Image Analogies Texture synthesis Super-resolution Texture transfer Image colorization Simple filters (blur, emboss) More details http://mrl.nyu.edu/projects/image-analogies/
39	Applications of Texture Modeling Super-resolution Freeman & Pasztor, 1999 Baker & Kanade, 2000 Image/video compression Texture recognition, segmentation DeBonet Restoration removing scratches, holes, filtering Zhu et al. Art/entertainment