3D Multi-frequency Fully Correlated Causal Random Field Texture Model

0522438 - ÚTIA 2021 RIV CH eng C - Conference Paper (international conference)
Haindl, Michal - Havlíček, Vojtěch
3D Multi-frequency Fully Correlated Causal Random Field Texture Model.
Pattern Recognition. Cham: Springer International Publishing, 2020 - (Palaiahnakote, S.; Sanniti di Baja, G.; Wang, L.; Yan, W.), s. 423-434, č. článku 33. Lecture Notes in Computer Science, 12047. ISBN 978-3-030-41298-2. ISSN 0302-9743. E-ISSN 1611-3349.
[The 5th Asian Conference on Pattern Recognition (ACPR 2019). Auckland (NZ), 26.11.2019-29.11.2019]
R&D Projects: GA ČR(CZ) GA19-12340S
Institutional support: RVO:67985556
Keywords : texture modeling * Markov random field * Bidirectional Texture Function
OECD category: Applied mathematics
http://library.utia.cas.cz/separaty/2020/RO/haindl-0522438.pdf

We propose a fast novel multispectral texture model with an analytical solution for both parameter estimation as well as unlimited synthesis. This Gaussian random field type of model combines a principal random field containing measured multispectral pixels with an auxiliary random field resulting from a given function whose argument is the principal field data.
The model can serve as a stand-alone texture model or a local model for more complex compound random field or bidirectional texture function models.
The model can be beneficial not only for texture synthesis, enlargement, editing, or compression but also for high accuracy texture recognition.
Permanent Link: http://hdl.handle.net/11104/0307296