1. TEXTURE SYNTHESIS PEI YEAN LEE

2. What is texture? Images containing repeating patterns Local & stationary

3. What is texture synthesis? An alternative way to create textures Construction of large regions of texture from small example images. Texture Synthesis Input Result

4. 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.

5. Application 1: Computer Graphics Make things `look ’ real –Rendering life-like animations

6. Application 2: Image Processing Image compression Image restoration and editing

7. Application 3: Computer Vision To verify texture models for various tasks such as texture segmentation, recognition and Classification.

8. Some definitions Image pyramidImage pyramid –A collection of images of reduced resolutions of the original 1:1 image – 1:2 n Gaussian pyramidGaussian pyramid low-pass –Consists of a set of low-pass filtered versions of the image –Pg. 161 (Fig 7.17)

9. Laplacian pyramidLaplacian pyramid band-pass –Consists of a set of band-pass filtered versions of the image –Pg. 198 (Fig. 9.8) Some definitions

10. Approach 1: Physical simulation Advantages: –produce texture directly on 3D meshes, thus avoid texture mapping distortion problem Disadvantages: –Applicable only to small texture class

11. 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

12. 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

13. Approach 3: Feature matching Heeger & Bergen (1995) marginal histograms –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.

14. Approach 3: Feature matching De Bonet (1997) cross-scale dependencies –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.

15. Approach 3: Feature matching Simoncelli & Portilla (1998) joint statistics –Generate textures by matching the joint statistics of the image pyramids –Advantages: Can capture global textural structures –Disadvantages: Fails to preserve local patterns

16. Web demo http://graphics.stanford.edu/project s/texture/