Download presentation
Presentation is loading. Please wait.
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/
Similar presentations
© 2025 SlidePlayer.com. Inc.
All rights reserved.