Design of a photo interpretation automaton

The extremely large volume of photographic material now being provided by r e connaissance and surveillance systems, coupled with limited, but significant, successes in designing machinery to recognize patterns has caused serious consideration to be given to the automation of certain portions of the photo interpretation task. While there is little present likelihood of successfully designing machines to interpret aerial photographs in a complete sense, there is ample evidence to support the conjecture that simple objects, and even some complex objects, in aerial photographs might be isolated and classified automatically. Even if machinery, produced in the near future, can only perform a preliminary sorting to rapidly winnow the input volume and to reduce human boredom and fatigue on simple recognition tasks, the development of such machinery may well be justified. The supporting evidence for the conjecture that simple objects can be identified in aerial photographs is based on work which has shown experimentally that present patternrecognition machinery—indeed that which existed several years ago—can be applied to the recognition of silhouetted, stylized objects which are militarily interesting. Murray has reported just such a capability for a simple linear discriminator.! Since the information required to design more capable recognition machines is readily available, it might seem that there is no problem of real interest r e maining to m a k e a rudimentary photointerpretation machine an accomplished fact. This, unfortunately, is not so. One of the most difficult problems is that which is r e ferred to as the segmentation problem. The problem of pattern segmentation appears in almost all interesting pattern recognition problems, and is simply stated as the problem of determining where the pattern of interest begins and ends (as in speech recognition problems) or how one defines those precise regions or areas in a photo which constitute the patterns of interest. The problem exists whenever there is more than one