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/