It has long been a dream to realize machines with flexible visual
perception capability. Research on digital image processing by computers
was initiated about 30 years ago, and since then a wide variety of image
processing algorithms have been devised. Using such image processing
algorithms and advanced hardware technologies, many practical ma- chines
with visual recognition capability have been implemented and are used in
various fields: optical character readers and design chart readers in
offices, position-sensing and inspection systems in factories, computer
tomography and medical X-ray and microscope examination systems in
hospitals, and so on. Although these machines are useful for specific
tasks, their capabilities are limited. That is, they can analyze only
simple images which are recorded under very carefully adjusted
photographic conditions: objects to be recognized are isolated against a
uniform background and under well-controlled artificial lighting. In the
late 1970s, many image understanding systems were de- veloped to study
the automatic interpretation of complex natural scenes. They introduced
artificial intelligence techniques to represent the knowl- edge about
scenes and to realize flexible control structures. The first author
developed an automatic aerial photograph interpretation system based on
the blackboard model (Naga1980). Although these systems could analyze
fairly complex scenes, their capabilities were still limited; the types
of recognizable objects were limited and various recognition vii viii
Preface errors occurred due to noise and the imperfection of
segmentation algorithms.