Texture What is texture? Easy to recognize, hard to define - - PDF document

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Texture What is texture? Easy to recognize, hard to define - - PDF document

Texture What is texture? Easy to recognize, hard to define Deterministic textures (thing-like) Stochastic textures (stuff-like) Tasks Discrimination / Segmentation Classification Texture synthesis


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Texture

  • What is texture?

– Easy to recognize, hard to define – Deterministic textures (“thing-like”) – Stochastic textures (“stuff-like”)

  • Tasks

– Discrimination / Segmentation – Classification – Texture synthesis – Shape from texture – Texture transfer – Video textures

Texture Discrimination

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Shape from Texture

Modeling Texture

  • What is texture?

– An image obeying some statistical properties – Similar structures repeated over and over again – Often has some degree of randomness

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Steerable (i.e., Oriented) Pyramids

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Texture Synthesis [Efros & Leung, ICCV 99]

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

sample image Generated image

SAMPLE x

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Markov Random Field

A Markov random field (MRF)

  • generalization of Markov chains to two or more dimensions

First-order MRF:

  • probability that pixel X takes a certain value given the values
  • f neighbors A, B, C, and D:

D C X A B X ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ X ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗

  • Higher order MRF’s have larger neighborhoods

Markov Chain

  • 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:

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Really Synthesizing One Pixel

sample image

– An exact neighborhood match might not be present – So we find the best matches using SSD error and randomly choose between them, preferring better matches with higher probability SAMPLE

Generated image

x

Growing Texture

– Starting from the initial image, “grow” the texture one pixel at a time

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Window Size Controls Regularity

More Synthesis Results

Increasing window size

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More Results

aluminum wire reptile skin

Failure Cases

Growing garbage Verbatim copying

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Image-Based Text Synthesis

  • Efros & Leung ’99 Extended

Efros & Leung ’99 Extended

  • Observation: neighbor pixels are highly correlated
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  • !

"

  • #
  • $

Minimal error boundary Minimal error boundary

" "

  • 2

2

"

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Philosophy Philosophy

  • The “Corrupt Professor’s Algorithm:”

– Plagiarize as much of the source image as you can – Then try to cover up the evidence

  • Rationale:

– Texture blocks are by definition correct samples of texture, so the only problem is connecting them together

Texture Transfer

Constraint Texture sample

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Texture Transfer Texture Transfer

  • Take the texture from one object

and “paint” it onto another object

– This requires separating texture and shape – That’s HARD, but we can cheat – Assume we can capture shape by boundary and rough shading

%&'( %&'(

  • +

+ = = + + = =

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= = + +

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