Abstract
The automatic extraction of information from complex pictorial scenes presents us with three distinct, related problems. These are: (1) scene segmentation, (2) identification of a scene’s component parts, and (3) interpretation and classification of the entire scene [Duda and Hart (1973); Rosenfeld (1972); Bullock (1971); Lemkin (1975)]. The decomposition of a pictorial field into sub-fields which are either homogeneous in their optical properties or logically coherent has been attempted with a wide assortment of algorithms [Thompson (1974); Kanal (1968); Rosenfeld (1969); Andrews (1970); Sacks-Lipkin and Rosenfeld (1970); Watanabe (1972); Watanabe (1969)]. Problems arise mostly from such sources as uncertain delineation of subfields and insufficient contrast between different subfields. Also, there exists a wide variety of imagery where scene components are characterized by information which is of a non-visual nature [Prewitt and Mendelsohn (1966), Bartels et al. (1972), Bellamy (1971), Sandritter and Kiefer (1970), Bartels and Wied (1973)], i.e., detection requires a preprocessing image transformation.
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Bartels, P.H., Subach, J.A. (1976). Significance Probability Mappings and Automated Interpretation of Complex Pictorial Scenes. In: Preston, K., Onoe, M. (eds) Digital Processing of Biomedical Images. Springer, Boston, MA. https://doi.org/10.1007/978-1-4684-0769-3_9
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DOI: https://doi.org/10.1007/978-1-4684-0769-3_9
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