Shimon Edelman
School of Cognitive and Computing Sciences
University of Sussex, Falmer, Brighton BN1 9QH, UK
shimone@cogs.susx.ac.uk
Sharon Duvdevani-Bar
Department of Applied Mathematics
Weizmann Institute of Science, Rehovot 76100, Israel
sharon@wisdom.weizmann.ac.il
Abstract
Visual objects can be represented by their similarities to a small
number of reference shapes or prototypes. This method yields
low-dimensional (and therefore computationally tractable)
representations, which support both the recognition of familiar
shapes and the categorization of novel ones. In this note, we show
how such representations can be used in a variety of tasks involving
novel objects: viewpoint-invariant recognition, recovery of a
canonical view, estimation of pose, and prediction of an arbitrary
view. The unifying principle in all these cases is the
representation of the view space of the novel object as an
interpolation of the view spaces of the reference shapes.