We present the DynTex database of high-quality dynamic texture videos. It consists of over 650 sequences of dynamic textures, mostly in everyday surroundings. Additionally, we propose a scheme for the manual annotation of the sequences based on a detailed analysis of the physical processes underlying the dynamic textures. Using this scheme we describe the texture sequences in terms of both visual structure and semantic content. The videos and annotations are made publicly available for scientific research.

We present an optical flow calculation method based on the variational approach. The method uses an implicit non-linear numerical scheme that makes no assumption on the analytical form of the optical constraints and combines local and global smoothness criteria in a natural way. Defining the optical constraints with normalised cross-correlation, we obtain a technique that is robust against illumination changes in colour and greyscale images.

Dynamic textures (DTs) are videos of natural or artificial processes, such as waves, smoke, fire, or walking crowd. While activities and motion events produced by moving shapes are well localised in space and time, the spatiotemporal extent of most natural DTs is less definite. The notion of periodicity, or regularity, has been extensively studied in static texture analysis and in activity analysis, where it proved very useful. At the same time, much less attention has been paid to temporal (quasi-) periodicity of dynamic textures, despite the obvious fact that this property is inherent in dynamic texture. We discuss the reasons, then present a framework for quantitative motion periodicity analysis of DTs. Using the optic flow and adapting the SVD-based algorithm for signal period estimation [17], we measure degree of periodicity of natural dynamic textures. Numerous test results are presented, including the application of the temporal periodicity features to DT classification. 1