2001
@article{BEL2001,
vgclass = {refpap},
author = {Ziv Bar-Joseph and Ran El-Yaniv and Dani Lischinski and
Mike Werman},
title = {Texture Mixing and Texture Movie Synthesis using
Statistical Learning},
journal = {IEEE Transactions on Visualization and Computer Graphics},
volume = {7},
number = {2},
pages = {120--135},
month = {April--June},
year = {2001},
url = {http://www.cs.huji.ac.il/labs/cglab/papers/texsyn/},
url1 = {http://www.cs.huji.ac.il/labs/cglab/papers/texsyn/texsyn.pdf},
url2 = {http://ieeexplore.ieee.org/xpl/abs_free.jsp?arNumber=928165},
abstract = {We present an algorithm based on statistical learning for
synthesizing static and time-varying textures matching the appearance
of an input texture. Our algorithm is general and automatic, and it
works well on various types of textures including 1D sound textures, 2D
texture images and 3D texture movies. The same method is also used to
generate 2D texture mixtures that simultaneously capture the appearance
of a number of different input textures. In our approach, input
textures are treated as sample signals generated by a stochastic
process. We first construct a tree representing a hierarchical
multi-scale transform of the signal using wavelets. From this tree, new
random trees are generated by learning and sampling the conditional
probabilities of the paths in the original tree. Transformation of
these random trees back into signals results in new random textures.
In the case of 2D texture synthesis our algorithm produces results that
are generally as good or better than those produced by previously
described methods in this field. For texture mixtures our results are
better and more general than those produced by earlier methods. For
texture movies, we present the first algorithm that is able to
automatically generate movie clips of dynamic phenomena such as
waterfalls, fire flames, a school of jellyfish, a crowd of people, etc.
Our results indicate that the proposed technique is effective and
robust.},
}