A COMPUTATIONAL AND EVOLUTIONARY PERSPECTIVE ON THE ROLE OF REPRESENTATION IN VISION

Recently, the assumed goal of computer vision, reconstructing a repres entation of the scene, has been critcized as unproductive and impracti cal. Critics have suggested that the reconstructive approach should be supplanted by a new purposive approach that emphasizes functionality and task driven perception at the cost of general vision. In response to these arguments, we claim that the recovery paradigm central to the reconstructive approach is viable, and, moreover, provides a promisin g framework for understanding and modeling general purpose vision in h umans and machines. An examination of the goals of vision from an evol utionary perspective and a case study involving the recovery of optic flow support this hypothesis. In particular, while we acknowledge that there are instances where the purposive approach may be appropriate, these are insufficient for implementing the wide range of visual tasks exhibited by humans (the kind of flexible vision system presumed to b e an end-goal of artificial intelligence). Furthermore, there are inst ances, such as recent work on the estimation of optic flow, where the recovery paradigm may yield useful and robust results. Thus, contrary to certain claims, the purposive approach does not obviate the need fo r recovery and reconstruction of flexible representations of the world . (C) 1994 Academic Press, Inc.