@inproceedings{Yuan:2006:PIP,
opteditor = {},
optpostscript = {},
optorganization = {},
author = {Xiaoru Yuan and Baoquan Chen},
optkey = {},
series = LNICS,
optannote = {},
address = SpringerAdr,
localfile = {papers/Yuan.2006.PIP.pdf},
optisbn = {},
publisher = SpringerPub,
optkeywords = {},
doi = {http://dx.doi.org/10.1007/11919476_6},
optmonth = {},
optciteseer = {},
opturl = {},
volume = {4291},
optcrossref = {},
optwww = {},
booktitle = {Advances in Visual Computing, Proceedings of the Second
International Symposium on Visual Computing, Part 1 (ISVC 2006,
November 6--8, 2006, Lake Tahoe, NV, USA)},
optnumber = {},
abstract = {We present a novel Procedural Image Processing (PIP) method and
demonstrate its applications in visualization. PIP modulates the
sampling positions of a conventional image processing kernel (e.g.
edge detection filter) through a procedural perturbation function.
When properly designed, PIP can produce a variety of styles for
edge depiction, varying on width, solidity, and pattern, etc. In
addition to producing artistic stylization, in this paper we
demonstrate that PIP can be employed to achieve various
visualization tasks, such as contour enhancement, focus+context
visualization, importance driven visualization and uncertainty
visualization. PIP produces unique effects that often either
cannot be easily achieved through conventional filters or would
require multiple pass filtering. PIP perturbation functions are
either defined by analytical expressions or encoded in
pre-generated images. We leverage the programmable fragment shader
of the current graphics hardware for achieving the operations in
real-time.},
title = {{P}rocedural {I}mage {P}rocessing for {V}isualization},
year = {2006},
pages = {50--59},
}
|