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Bibliografie

Conference Paper (international conference)

Texture Spectral Decorrelation Criteria

Haindl Michal, Havlíček Michal

: Pattern Recognition : 27th International Conference, ICPR 2024, p. 324-333 , Eds: Antonacopoulos Apostolos, Chaudhuri Subhasis, Chellappa Rama, Liu Cheng-Lin, Bhattacharya Saumik, Pal Umapada

: International Conference on Pattern Recognition 2024 /27./, (Kolkata, IN, 20241201)

: Texture spectral quality comparison, Texture modeling, Texture synthesis, Bidirectional texture function

: 10.1007/978-3-031-78172-8_21

: https://library.utia.cas.cz/separaty/2024/RO/haindl-0602524.pdf

(eng): We introduce texture spectral criteria, which allow us to predict whether simplified spectrally factorized random field-based texture models, a set of two-dimensional models, can faithfully replicate texture spectra compared to their fully spectrally correlated 3D counterparts. These probabilistic models incorporate essential two- or three-dimensional building factors for modeling the seven-dimensional Bidirectional Texture Function (BTF), the most advanced representation in real-world material visual properties modeling. While these models seamlessly approximate original measured massive data and extend them to arbitrary sizes or simulate unmeasured textures, evaluating typically involves time-consuming synthesis and psycho-physical evaluation. The proposed criteria provide an alternative approach, enabling us to bypass the spectral quality evaluation step.

: BD

: 20202