International Journal of Computer Vision
Texture Mixing and Texture Movie Synthesis Using Statistical Learning
IEEE Transactions on Visualization and Computer Graphics
Analysis and Synthesis of Textured Motion: Particles and Waves
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures
IEEE Transactions on Pattern Analysis and Machine Intelligence
From ramp discontinuities to segmentation tree
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Dynamic texture classification using dynamic fractal analysis
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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This paper proposes the problem of modeling video sequences of dynamic swarms (DSs). We define a DS as a large layout of stochastically repetitive spatial configurations of dynamic objects (swarm elements) whose motions exhibit local spatiotemporal interdependency and stationarity, i.e., the motions are similar in any small spatiotemporal neighborhood. Examples of DS abound in nature, e.g., herds of animals and flocks of birds. To capture the local spatiotemporal properties of the DS, we present a probabilistic model that learns both the spatial layout of swarm elements (based on low-level image segmentation) and their joint dynamics that are modeled as linear transformations. To this end, a spatiotemporal neighborhood is associated with each swarm element, in which local stationarity is enforced both spatially and temporally. We assume that the prior on the swarm dynamics is distributed according to an MRF in both space and time. Embedding this model in a MAP framework, we iterate between learning the spatial layout of the swarm and its dynamics. We learn the swarm transformations using ICM, which iterates between estimating these transformations and updating their distribution in the spatiotemporal neighborhoods. We demonstrate the validity of our method by conducting experiments on real and synthetic video sequences. Real sequences of birds, geese, robot swarms, and pedestrians evaluate the applicability of our model to real world data.