An introduction to model-based imaging.
The purpose of this paper is to clarify the distinction between the recognition of form, i.e. pattern recognition, and the interpretation of visual scenes, i.e. image understanding. Pattern recognition is part of image understanding, but the latter also includes cognitive tasks such as learning and inference. The key to developing image-understanding systems is to concentrate on the representation and use of models. This paper is a brief outline of the components of a model-based image-understanding system. First, the notions of iconic, categorical and symbolic knowledge are described. Although they appear to be disparate, the common notion is that the image understanding is based on recognizing concepts and not recognizing form. Next, the notion of a concept is defined, followed by representation techniques and control strategies for using concepts. Last, an example is given of an image-understanding system that learns to recognize concepts such as radiographic projections of teeth in panoramic radiographs.
Dunn
S M
SM
Department of Biomedical Engineering, Rutgers University, Piscataway, New Jersey.
D005103:Expert Systems; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008954:Models, Biological; D010363:Pattern Recognition, Automated; D011861:Radiography, Dental