Advances in sensing, signal processing, and computer technology during
the past half century have stimulated numerous attempts to design
general-purpose ma- chines that see. These attempts have met with at
best modest success and more typically outright failure. The
difficulties encountered in building working com- puter vision systems
based on state-of-the-art techniques came as a surprise. Perhaps the
most frustrating aspect of the problem is that machine vision sys- tems
cannot deal with numerous visual tasks that humans perform rapidly and
effortlessly. In reaction to this perceived discrepancy in performance,
various researchers (notably Marr, 1982) suggested that the design of
machine-vision systems should be based on principles drawn from the
study of biological systems. This "neuro- morphic" or "anthropomorphic"
approach has proven fruitful: the use of pyramid (multiresolution) image
representation methods in image compression is one ex- ample of a
successful application based on principles primarily derived from the
study of biological vision systems. It is still the case, however, that
the perfor- of computer vision systems falls far short of that of the
natural systems mance they are intended to mimic, suggesting that it is
time to look even more closely at the remaining differences between
artificial and biological vision systems.