This paper explores the ways in which resource limitations influence the nature of perceptual and cognitive processes. A framework is developed that allows early visual processing to be analyzed in terms of these limitations. In this approach, there is no one "best" system for any visual process. Rather, a spectrum of systems exists, differing in the particular trade-offs made between performance and resource requirements.

Neural networks have been used to investigate some of the assumptions m a d e in Biederman's recognition by components (RBC) theory of visual perception. Biederman's RBC theory states, in part, that object vertices are critical features for the 2D region segmentation phase of human object recognition. This paper presents computational evidence for Biederman's claim that viewpoint-invariant vertices are critical to object recognition. In particular, w e present a neural network model for 2D object recognition using object vertices as image primitives. The neural net is able to recognize objects with as much as 65% mid-segment centered contour deletion, while it is unable to recognize objects with as little as 25% vertex centered deletion. In addition the neural net exhibits shift, scale and partial rotational invariance.