Contextual Image Motion Estimation And Analysis Using Spatio-Temporal Primitives

Abstract

This research extends previous work on the modelling and analysis of time-varying imagery using distributed parameter systems (DPS) theory. An integrated or "weak" solution approach to the DPS model yields a computationally efficient algorithm for feature extraction and estimation of time-varying image motion which allows image function step discontinuities with respect to both spatial and temporal arguments. These algor-ithms represent a region-oriented as opposed to point-by-point approach to image motion analysis. Experimental results using real-world time-varying imagery confirm the validity of the approach. The particular effort reported herein concerns analysis of the spatio-temporal features extracted from the weak solution approach. Information related both quantitatively to motion estimation and qualitatively to motion and object(s) structure are shown to evolve from this approach. Thus, the blending of the "motion is basic" and "static is basic" approaches in dynamic imagery is enabled. To remove some of the "nearsightedness" of a parameter estimation approach to motion analysis, contextual motion analysis is employed for both extraction of dynamic scene regions and edge types and labelling of motion regions. The present status of each of these tasks is described.