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TEXTURE SYNTHESIS
PEI YEAN LEE
What is texture?
•Images containing repeating patterns
•Local & stationary
What is texture
synthesis?
• An alternative way to create textures
• Construction of large regions of texture
from small example images.
Texture Synthesis
Input
Result
Goal of texture
synthesis ?
• Given: a texture sample
• Find : synthesize a new texture that,
when perceived by a human
observer, appears to be
generated by the same
underlying process.
Application 1:
Computer Graphics
• Make things `look’ real
– Rendering life-like animations
Application 2:
Image Processing
• Image compression
• Image restoration and editing
Application 3:
Computer Vision
• To verify texture models for
various tasks such as texture
segmentation, recognition and
Classification.
Some definitions
• Image pyramid
– A collection of images of reduced
resolutions of the original 1:1 image – 1:2n
• Gaussian pyramid
– Consists of a set of low-pass filtered
versions of the image
– Pg. 161 (Fig 7.17)
Some definitions
• Laplacian pyramid
– Consists of a set of band-pass filtered
versions of the image
– Pg. 198 (Fig. 9.8)
Approach 1:
Physical simulation
• Advantages:
– produce texture directly on 3D meshes,
thus avoid texture mapping distortion
problem
• Disadvantages:
– Applicable only to small texture class
Approach 2:
Probability sampling
• Zhu, Wu & Mumford (1998)
– Markov Random Field (MRF)
– Gibbs Sampling
– Advantages:
• Good approx. for wide range of textures
– Disadvantages:
• Computationally expensive
Approach 3:
Feature matching
• Model textures as a set of features
and generate new images by matching
the features in an example feature.
• Advantages:
– More efficient than MRF
Approach 3:
Feature matching
• Heeger & Bergen (1995)
– model textures by matching marginal
histograms of image pyramid
– Advantages:
• Works well for highly stochastic textures
– Disadvantages:
• Fails on more structured textures patterns such
as bricks.
Approach 3:
Feature matching
• De Bonet (1997)
– Synthesizes new images by randomizing an
input texture sample while preserving
cross-scale dependencies
– Advantages:
• Works better on structured textures
– Disadvantages:
• Can produce boundary artifacts if the input
texture is not tileable.
Approach 3:
Feature matching
• Simoncelli & Portilla (1998)
– Generate textures by matching the joint
statistics of the image pyramids
– Advantages:
• Can capture global textural structures
– Disadvantages:
• Fails to preserve local patterns
Web demo
• http://graphics.stanford.edu/project
s/texture/