Part of Advances in Neural Information Processing Systems 4 (NIPS 1991)
Ronen Basri, Shimon Ullman
Visual object recognition involves the identification of images of 3-D ob(cid:173) jects seen from arbitrary viewpoints. We suggest an approach to object recognition in which a view is represented as a collection of points given by their location in the image. An object is modeled by a set of 2-D views together with the correspondence between the views. We show that any novel view of the object can be expressed as a linear combination of the stored views. Consequently, we build a linear operator that distinguishes between views of a specific object and views of other objects. This opera(cid:173) tor can be implemented using neural network architectures with relatively simple structures.