Near Regular Texture Synthesis
The objective of texture synthesis is to generate an arbitrarily
sized image that reproduces the texture of a relatively
small sample image. During the last years, many researchers
in computer vision and computer graphics have
proposed methods for texture synthesis and achieved impressive
results for many kinds of textures. Sample-based
methods compose the output only from extracted pieces of
the input sample in contrast to methods that infer a statistical
model for the input texture. Near-regular textures, i.e.
textures that contain global regular structures and additional
irregular stochastic structures (e.g. due to the yarn structure
in cloth etc.) are still difficult to synthesize. This kind of texture
is ubiquitous in the real world, such as cloth, bricks, tiles
etc. A faithful reproduction of these textures should preserve
both the regular pattern of the texture as well as the stochastic
nature of the irregular structure. The latter might be subtle
yet very important for the natural appearance of the result.
Simple tiling would fail to reproduce the stochastic nature
of the irregular structures and statistical methods would fail
to reproduce the regular structure.
In this work, we propose a method to synthesize nearregular
textures in a constrained random sampling approach.
In a first analysis step, we treat the texture as regular and
analyze the global regular structure of the input sample texture
to estimate two translation vectors defining the size and
shape of a texture tile. In a subsequent synthesis step, this
structure is exploited to guide or constrain a random sampling
process so that random samples of the input are introduced
into the output preserving the regular structure previously
detected. This ensures the stochastic nature of the
irregularities in the output yet preserving the regular pattern
of the input texture. Although our method was developed for
near-regular textures we observed that it produces also very
good results for irregular textures.
Results: NRT Type I textures are better synthesized with our method than with
other existing patch-based approaches - Part I. In columns from left to right: input
texture sample with the detected lattice superimposed in red, output of best selfsimilar
tile repetition, our main synthesis technique (RSGF) and an implementation
of IQ [Efros(2001)].
Results achieved with our method for irregular textures.
D. Lopez Recas, A. Hilsmann and P. Eisert:"Near-Regular Texture Synthesis by Random Sampling and Gap Filling", Vision, Modeling, and Visualization Workshop 2011 , Berlin, Germany, October 2011.