When ever an object moves, it successively covers and uncovers surfaces that are farther away. This occlusion
and dis-occlusion always occurs precisely at the boundaries of the moving object and as such provide information
not only about the shape of the object but also about its velocity, transparency, and relative depth. Humans can
and do use this information, and the process has come to be called Spatiotemporal Boundary Formation (SBF).
Previous authors have used the wealth of experimental investigations into SBF to create a mathematical model
of the process. In this article we proposed a novel method to recover the orientation and velocity the local edge
segments of the moving objects which is more flexible, more robust, more compact, and allows the recovery of
edges that do not have a constant velocity. The method can be used in object segmentation algorithms or as a
pre-filter for machine-learning-based recognition algorithms in order to improve the overall result.