Artificial Intelligence In Image Processing
John F. Gilmore · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1985
Image processing technology concentrates on the development of data extraction techniques applied toward the statistical classification of visual imagery. In classical image processing systems, an image is [1] preprocessed to remove noise, [2] segmented to produce close object boundaries, [3] analyzed to extract a representative feature vector, and [4] compared to ideal object feature vectors by a classifier to determine the nearest object classification and its associated confidence level. This type of processing attempts to formulate a two-dimensional interpretation of three-dimensional scenes using local statistical analysis, an entirely numerical process. Symbolic information dealing with contextual relationships, object attributes, and physical constraints is ignored in such an approach. This paper describes a number of artificial intelligence techniques which allow symbolic information to be exploited in conjunction with numerical data to improve object classification performance.