Pyramid-based texture analysis/synthesis
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Equivalence of Julesz Ensembles and FRAME Models
International Journal of Computer Vision - Special issue on Genomic Signal Processing
International Journal of Computer Vision
A Generative Method for Textured Motion: Analysis and Synthesis
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Graphcut textures: image and video synthesis using graph cuts
ACM SIGGRAPH 2003 Papers
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Analysis and Synthesis of Textured Motion: Particles and Waves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimax Entropy Principle and Its Application to Texture Modeling
Neural Computation
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In this paper we consider the problem of temporal texture modeling and synthesis. A temporal texture (or dynamic texture) is seen as the output of a dynamical system driven by white noise. Experimental evidence shows that linear models such as those introduced in earlier work are sometimes inadequate to fully describe the time evolution of the dynamic scene. Extending upon recent work which is available in the literature, we tackle the synthesis using non-linear dynamical models. The non-linear model is never given explicitly but rather we describe a methodology to generate samples from the model. The method requires estimating the “state” distribution and a linear dynamical model from the original clip which are then used respectively as target distribution and proposal mechanism in a rejection sampling step. We also report extensive experimental results comparing the proposed approach with the results obtained using linear models (Doretto et al.) and the “closed-loop” approach presented at ECCV 2004 by Yuan et al.