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Texture Synthesis by Non-parametric Sampling / Image Quilting for Texture Synthesis & Transfer by Efros and Leung / Efros and Freeman ICCV ’99 / SIGGRAPH ’01. Presentation by Gyozo Gidofalvi Computer Science and Engineering Department University of California, San Diego email@example.com
Texture Synthesis by Non-parametric Sampling / Image Quiltingfor Texture Synthesis & TransferbyEfros and Leung / Efros and Freeman ICCV ’99 / SIGGRAPH ’01
Presentation by Gyozo Gidofalvi
Computer Science and Engineering Department
University of California, San Diego
November 15, 2001
Given an input sample texture synthesize a texture that is sufficiently different from the given sample texture, yet appears perceptually to be generated by the same underlying stochastic process.
True (infinite) texture
finite sample image
To synthesize a pixel p, search the sample image for pixels with similar neighborhood to p, construct a histogram for the distribution of these pixels, finally sample this distribution to obtain a value for p.
Similarity is based on the Gaussian-weighted sum squared difference, to preserve local structure.
Increasing window size
Used in Lapped Textures [Praun et.al,’00]
The approach works well on stochastic textures but fails on structures.
constrained by overlap
Easy to optimize using NN search [Liang et.al., ’01]
use dynamic programming to compute minimal error boundary cut
Portilla & Simoncelli
Xu, Guo & Shum
Wei & Levoy
Take the texture from one object and “paint” it onto another object
Idea: just add another constraint when sampling: similarity to underlying image at that spot
Correspondence can be based on: image intensity, blured image intensity, local image orientation angles, etc…
There is a tradeoff between the legitimacy of synthesized texture and the correctness of the correspondence mapping.
We have seen:
Alexei Efros and his fellow researchers for sharing their slides.
A. A. Efros and W. T. Freeman. Image Quilting for Texture Synthesis and Transfer, SIGGRAPH01.
A. A. Efros and T. K. Leung. Texture Synthesis by Non-parametric Sampling. In ICCV99.
S. Livens. Image Analysis for Material Characterization, 1998. http://www.ruca.ua.ac.be/visielab/livens/phd1.ps.gz
C. E. Shannon, A mathematical theory of computation. Bell Sys. Tech. Journal, 27, 1948.
M. Ashikhmin. Synthesizing natural textures. In Symposium on Interactive 3D Graphics, 2001.
J. S. De Bonet. Multiresolution sampling procedure for analysis and synthesis of texture images. In SIGGRAPH 97, pages 361-368, 1997.
D. D. Garber. Computational Models for Texture Analysis and Texture Synthesis. PhD thesis, University of Southern California, Image Processing Institute, 1981.
P. Harrison. A non-hierarchical procedure for re-synthesis of complex textures. In WSCG 2001 Conference Proceedings, pages 190-197, 2001.
D. J. Heeger and J. R. Bergen. Pyramid based texture analysis/synthesis. In SIGGRAPH 95, pages 229-238, 1995.
A Hertzmann, C. E. Jacobs, D. Oliver, B. Curless, and D. H. Salesin. Image analogies. In SIGGRAPH 01, 2001.
L. Liang , C. Liu, Y, Xu, B. Guo, and H.-Y. Shum. Real-time texture synthesis by patch-based sampling. Technical Report MSR-TR-2001-40, Microsoft Research, March 2001.
Y. XU, B. Guo, and H.-Y. Shum. Chaos Mosaic: Fast and memory efficient texture synthesis. Technical Report MSR-TR-2000-32, Microsoft Research, April 2000.