A Neural System for the Recognition of Partially Occluded Objects in Cluttered Scenes: A Pilot Study

We present a system for the interpretation of camera images of scenes composed of several known objects with mutual occlusion. The scenes are analyzed by the recognition of the objects present and by the determination of their occlusion relations. Objects are internally represented by stored model graphs. These are formed in a semi-automatic way by showing objects against a varying background. Objects are recognized by dynamic link matching. Our experiments show that our system is very successful in analyzing cluttered scenes. The system architecture goes beyond classical neural networks by making extensive use of flexible links between units, as proposed in the dynamic link architecture. The present implementation is, however, rather algorithmic in style and is to be regarded as a pilot study that is preparing the way for a detailed implementation of the architecture.