Knowledge-directed vision: control, learning, and integration

The knowledge-directed approach to image interpretation popular in the 1980's, sought to identify objects in unconstrained two-dimensional (2-D) images and to determine the three-dimensional (3-D) relationships between these objects and the camera by applying large amounts of object- and domain-specific knowledge to the interpretation problem. Among the primary issues faced by these systems were variations among instances of an object class and differences in how object classes were defined in terms of shape, color, function, texture, size, and/or substructures. This paper argues that knowledge-directed vision systems typically failed for two reasons. The first is that the low- and mid-level vision procedures that were relied upon to perform the basic tasks a vision were too immature at the time to support the ambitious interpretation goals of these systems. This problem, we conjecture, has been largely solved by recent advances in the field of 3-D computer vision particularly in stereo and shape reconstruction from multiple views. The other impediment was that the control problem for vision procedures was never properly addressed as an independent problem. This paper reviews the issues confronted by knowledge-directed vision systems, and concludes that inadequate vision procedures and the lack of a control formalism blocked their further development. We then briefly introduce several new projects which, although still in the early stage of development, are addressing the complex control issues that continue to obstruct the development of robust knowledge-directed vision systems.