24 The MIT Robot P. H. Winston

INTRODUCTION Research in machine vision is an important activity in artificial intelligence laboratories for two major reasons. First, understanding vision is a worthy subject for its own sake. The point of view of artificial intelligence allows a fresh new look at old questions and exposes a great deal about vision in general, independent of whether man or machine is the seeing agent. Second, the same problems found in understanding vision are of central interest in the development of a broad theory of intelligence. Making a machine see brings one to grips with problems like that of knowledge interaction on many levels and of large system organization. In vision these key issues are exhibited with enough substance to be nontrivial and enough simplicity to be tractable. Both goals are framed in terms of a world of bricks, wedges, and other simple shapes like those found in children's toy boxes. Good purposeful description is often fundamental to research in artificial intelligence, and learning how to do description constitutes a major part of our effort in vision research. This essay begins with a discussion of that part of scene analysis known as body finding. The intention is to show how our understanding has evolved away from blind fumbling toward substantive theory. Finding groups of objects and using the groups to get at the properties of their members illustrates concretely how some of the ideas about systems work out in detail. The topic of learning follows. Discussing learning is especially appropriate here not only because it is an important piece of artificial intelligence theory but also because it illustrates a particular use for the elaborate analysis machinery dealt with in the previous sections. Finally a scenario exhibits the flavor of the system in a situation where a simple structure is copied from spare parts. The body-finding story begins with an ad hoc but crisp syntactic theory and ends in a simple, appealing theory with serious semantic roots. In this the history of the body-finding problem seems paradigmatic of vision system progress in general. Adolfo Guzman started the work in this area (Guzman 1968). I review his program here in order to anchor the discussion and show how better programs emerge through the interaction of observation, experiment, and theory. The task is simply to partition the observed regions of a scene into distinct bodies.