Matching and Interpretation of Planar Motion Using Tensor Voting
Abstract
An ellipse that is rotating within an image can be interpreted in different
ways under different conditions. We are interested in the 'thin vs. fat
ellipse' phenomenon; a thin rotating ellipse is perceived as undergoing
rigid rotation, while a fat one is perceived as undergoing nonrigid
deformation. Our computational model tries to emulate human perception using
the tensor voting framework. The original 4-D tensor voting formalism is
found not sufficient to solve this problem, and we augment it here to
explicitly handle rotation and deformation.