R. Mann and A. Jepson, Non-accidental Features in Learning AAAI Fall Symposium on Machine Learning in Vision, Oct 22-24, 1993, Raleigh, NC.

Abstract: Understanding observations of interacting objects requires one to reason about the force-dynamic relations between objects. We present an implemented computational theory that derives force-dynamic interpretations directly from camera input. Interpretations are expressed in terms of assertions about the kinematic and dynamic properties of objects. The feasibility of interpretations can be determined relative to Newtonian mechanics by a reduction to linear programming. Multiple feasible solutions are compared using a preference hierarchy to select plausible interpretations. We provide computational examples to demonstrate that our ontology is sufficiently rich to describe a wide variety of image sequences.