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TEXTURE SYNTHESISTEXTURE SYNTHESISTEXTURE SYNTHESISTEXTURE SYNTHESIS
PEI YEAN LEEPEI YEAN LEE
What is texture?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 pyramidImage pyramid
– A collection of images of reduced resolutions of the original 1:1 image – 1:2n
• Gaussian pyramidGaussian pyramid
– Consists of a set of low-passlow-pass filtered versions of the image
– Pg. 161 (Fig 7.17)
• Laplacian pyramidLaplacian pyramid
– Consists of a set of band-passband-pass filtered versions of the image
– Pg. 198 (Fig. 9.8)
Some definitions
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 marginal histograms 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 dependenciescross-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 joint statisticsstatistics of the image pyramids
– Advantages:• Can capture global textural structures
– Disadvantages:• Fails to preserve local patterns
Web demo
• http://graphics.stanford.edu/projects/texture/