Summary

Recognition of objects based on their images is one of the central problems in modern Computer Vision. Objects can be characterized by their geometric, photometric, and dynamic properties. While a vast literature exists on recognition based on geometry and photometry, much less has been said about recognizing scenes based upon their dynamics. (Saisan et al., 2001) formulates the problem of recognizing a sequence of images based on a joint photometric-dynamic model. This enables distinguishing not just steam from foliage, but also fast turbulent steam from haze, or to detect the presence of strong winds by looking at trees. Sequences are not represented by local features or optical flow. Instead, they are supposed to be realizations from stationary stochastic processes. Recognition is not based on classifying individual realizations, but statistical models that generate them. This entails studying the structure of the space of models, and defining distances between model instances. It is shown that ignoring the model space structure leads to poor recognition performance.

References

  1. CVPR
    Dynamic texture recognition Saisan, P., Doretto, G., Wu, Y. N., and Soatto, S. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001. abstract bibTeX pdf