Temporally multiple dynamic textures synthesis using piecewise linear dynamic systems
Real-world nonlinear dynamic textures (DTs) usually consist of temporally multiple linear DTs which cannot be correctly modeled by previous works. In this paper, we propose piecewise linear dynamic systems (PLDS) to model temporally multiple DTs. PLDS simultaneously decides the temporal segmentation, models each DT segment with an LDS and the whole DT by switching between the LDS'. Experimental results verify that PLDS can capture the stochastic and dynamic nature of temporally multiple DTs and it synthesizes nonlinear DTs without decay or divergence. An EM-like algorithm iterating between sequence division and LDS' fitting is adopted to learn the model parameters.