Texture Analysis and Synthesis - PowerPoint PPT Presentation

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Texture Analysis and Synthesis

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Compile histogram of intensities. output by each filter. To synthesize ... Adjust histograms to match original image. Re-synthesize image from filter outputs ... – PowerPoint PPT presentation

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Title: Texture Analysis and Synthesis


1
Texture Analysis and Synthesis

2
Texture
  • Texture pattern thatlooks the sameat all
    locations
  • May be structured or random

Wei Levoy
3
Applications of Textures
  • Texture analysis
  • Detemining statistical properties of textures
  • Segmentation
  • Recognition
  • Shape from texture
  • Texture synthesis

4
Oriented Filter Banks
Multiresolution Oriented Filter Bank
OriginalImage
Steerable Pyramid
5
Steerable Pyramid Texture Analysis
  • Pass image through filter bank
  • Compile histogram of intensitiesoutput by each
    filter
  • To synthesize new texture
  • Start with random noise image
  • Adjust histograms to match original image
  • Re-synthesize image from filter outputs

6
Texture Analysis / Synthesis
Original Texture
Synthesized Texture
Heeger and Bergen
7
Textons
  • Elements (textons) either identical or come
    from some statistical distribution
  • Can analyze in natural images

Olhausen Field
8
Clustering Textons
  • Output of bank of n filters can be thought of as
    vector in n-dimensional space
  • Can cluster these vectors using k-meansMalik et
    al.
  • Result dictionary of most common textures

9
Clustering Textons
Image
Texton to Pixel Mapping
Clustered Textons
Malik et al.
10
Using Texture in Segmentation
  • Compute histogram of how many times each of the k
    clusters occurs in a neighborhood
  • Define similarity of histograms hi and hj using
    c2
  • Different histograms ? separate regions

11
Texture Segmentation
Malik et al.
12
Markov Random Fields
  • Different way of thinking about textures
  • Premise probability distribution of a
    pixeldepends on values of neighbors
  • Probability the same throughout image
  • Extension of Markov chains

13
Texture Synthesis Based on MRF
  • For each pixel in destination
  • Take already-synthesized neighbors
  • Find closest match in original texture
  • Copy pixel to destination
  • Efros Leung 1999,speedup by Wei Levoy

Wei Levoy
14
Shape from Texture
  • Look at deformation of individual textons or at
    distribution of textons on a surface
  • Perspective distortion strong depth cue
  • Foreshortening gives surface normal
  • Sparse depth information (only at textons)
  • Impose smoothness (regularization) constraint
  • Inward/outward ambiguity

15
Shape from Texture Results
Forsyth
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