The evolution of geometric and photometric attributes of 2D organizational structures can not only provide evidence for the underlying structure and motion but also help infer complex motions. This research will develop a computational theory to extract structural and motion information from long image sequences by exploiting perceptual organizational principles. Simple motions will be grouped to infer about complex motion. An efficient computational framework will be developed to monitor the evolution of the organizational structures. Detection of the 2-D organizations isbased on graph theory and voting methods, and a modification of Bayesian networks. These 2D organizations are then tracked through the image sequence to generate traces of their photometric and geometric properties. This research will open up possibilities for the development of new tools for motion understanding using organizational principles.
