@inproceedings{Sziranyi:2001:OPR,
opteditor = {},
optpostscript = {},
optorganization = {},
author = {Tam{\'a}s Szir{\'a}nyi and Zolt{\'a}n T{\'o}th},
optkey = {},
series = LNICS,
optannote = {},
address = {Berlin},
localfile = {papers/Sziranyi.2001.OPR.pdf},
optisbn = {},
publisher = {Springer-Verlag},
optkeywords = {},
doi =
{http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=2134&spage=0201},
optmonth = {},
citeseer = {http://citeseer.csail.mit.edu/484023.html},
opturl = {},
volume = {2134},
optcrossref = {},
optwww = {},
booktitle = {Proceedings of the Third International Workshop on Energy
Minimization Methods in Computer Vision and Pattern Recognition
(EMMCVPR'01, London, UK, 2001)},
optnumber = {},
abstract = {We have developed a new stochastic image rendering method for the
compression, description and segmentation of images. This
paintbrush-like image transformation is based on a random
searching to insert brush-strokes into a generated image at
decreasing scale of brush-sizes, without predefined models or
interaction. We introduced a sequential multiscale image
decomposition method, based on simulated rectangular-shaped
paintbrush strokes. The resulting images look like good-quality
paintings with well-defined contours, at an acceptable distortion
compared to the original image. The image can be described with
the parameters of the consecutive paintbrush strokes, resulting in
a parameter-series that can be used for compression. The painting
process can be applied for image representation, segmentation and
contour detection. Our original method is based on stochastic
exhaustive searching which takes a long time of convergence. In
this paper we propose a modified algorithm of speed up of about 2x
where the faster convergence is supported by a dynamic Metropolis
Hastings rule.},
title = {{O}ptimization of {P}aintbrush {R}endering of {I}mages by {D}ynamic
{MCMC} {M}ethods},
year = {2001},
pages = {201--215},
}
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