Knowledge-directed vision: control, learning, and integration
Proceedings of the IEEE, 84 (11):
1625--1637 (1996)Abstract
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
