Recent results on neural network architectures for vision and pattern recognition

Summary form only given. The author describes the development of a general-purpose automatic vision architecture. He clarifies how, with this architecture, scenic data about boundaries, textures, shading, depth, multiple spatial scales, and motion can be cooperatively synthesized in real time into a coherent representation of three-dimensional form. In order to computationally understand the labile relationships known to occur between recognized emergent segmentations and the seen brightness and colors that result from filling-in, it has been necessary to develop a qualitatively different type of vision theory. This theory provides a fresh analysis of how the human visual system is designed to detect relatively invariant surface colors under variable illumination conditions, to detect relatively invariant object boundary structures amid noise caused by the eye´s own optics or occluding objects, and to recognize familiar objects or events in the environment. These three functions are performed by the three main subsystems of the system, the feature contour system, the boundary contour system and the object recognition system