  Learning object models from views in 3D visual object recognition is usually
formulated either as a function approximation problem of a function describing
the view-manifold of an object, or as that of learning a class-conditional
density. This paper describes an alternative framework for learning in visual
object recognition, that of learning the view-generalization function. Using
the view-generalization function, an observer can perform Bayes-optimal 3D
object recognition given one or more 2D training views directly, without the
need for a separate model acquisition step. The paper shows that view
generalization functions can be computationally practical by restating two
widely-used methods, the eigenspace and linear combination of views approaches,
in a view generalization framework. The paper relates the approach to recent
methods for object recognition based on non-uniform blurring. The paper
presents results both on simulated 3D ``paperclip'' objects and real-world
images from the COIL-100 database showing that useful view-generalization
functions can be realistically be learned from a comparatively small number of
training examples.
