Recognizing novel 3-D objects under new illumination and viewing position using a small number of example views or even a single view

Abstract

A method is presented for class-based recognition using a small number of example views taken under several different viewing conditions. The main emphasis is on using a small number of examples. Previous work assumed that the set of examples is sufficient to span the entire space of possible objects, and that in generalizing to a new viewing conditions a sufficient number of previous examples under the new conditions will be available to the recognition system. Here we have considerably relaxed these assumptions and consequently obtained good class-based generalization from a small number of examples, even a single example view, for both viewing position and illumination changes. In addition, previous class-based approaches only focused on viewing position changes and did not deal with illumination changes. Here we used a class-based approach that can generalize for both illumination and viewing position changes. The method was applied to face and car model images. New views under viewing position and illumination changes were synthesized from a small number of examples.