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PEI YEAN LEE

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.

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PEI YEAN LEE

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  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 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)

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

  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) • 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) • 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) • 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/projects/texture/

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