Bidirectional Texture Function Simultaneous Autoregressive Model

0380289 - ÚTIA 2013 RIV DE eng C - Conference Paper (international conference)
Haindl, Michal - Havlíček, Michal
Bidirectional Texture Function Simultaneous Autoregressive Model.
Computational Intelligence for Multimedia Understanding. Berlin: Springer, 2012, s. 149-159. Lecture Notes in Computer Science, 7252. ISBN 978-3-642-32435-2. ISSN 0302-9743.
[MUSCLE. Pisa (IT), 13.12.2011-15.12.2011]
R&D Projects: GA MŠMT 1M0572; GA ČR GA102/08/0593; GA ČR GAP103/11/0335
Grant - others:CESNET(CZ) 387/2010
Institutional support: RVO:67985556
Keywords : bidirectional texture function * texture analysis * texture synthesis * data compression * virtual reality
Subject RIV: BD - Theory of Information
http://library.utia.cas.cz/separaty/2012/RO/haindl-bidirectional texture function simultaneous autoregressive model.pdf

The Bidirectional Texture Function (BTF) is the recent most advanced representation of visual properties of surface materials. It specifies their altering appearance due to varying illumination and viewing conditions. Corresponding huge BTF measurements require a mathematical representation allowing simultaneously extremal compression as well as high visual fidelity. We present a novel Markovian BTF model based on a set of underlying simultaneous autoregressive models (SAR). This complex but efficient BTF-SAR model combines several multispectral band limited spatial factors and range map sub-models to produce the required BTF texture space. The BTF-SAR model enables very high BTF space compression ratio, texture enlargement, and reconstruction of missing unmeasured parts of the BTF space.
Permanent Link: http://hdl.handle.net/11104/0211031