1 July 1990 Rule-based image interpretation with models of expected structure
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Consider the problem of localizing and identifying cell organelle in a transmission electron micrograph. Opera. . tions on regions constitute the key tasks in segmenting such images. The construction of meaningful entities from an initial fine partitioning of such an image poses problems that are generally linked to the type of objects to be identified. Restraining region manipulation algorithms to a particular class of images may simplify the process. However the loss of retargetability for the segmentation process is a serious handicap of such a solution. Segmentation must be based on a formal mechanism for reasoning about scenes (cells) their images (trans. . mission electron micrographs) objects in the scene (organelle) and their representation (image regions) if the system is to be suitable for a wide variety of domains. Mathematical morphology offers such a mechanism but the drawbacks of point set topology limit its success to low-level vision tasks. A modification based on expected morphological properties of point sets and not their fixed structure is useful for intermediate vision. This modification makes it possible to develop a theory for region manipulation. The main goal of growing and shrinking is to obtain regions with a given expected structure. The operations involve a search in the region space for candidates satisfying specified conditions and capable of producing final regions that fit the desired goal subject to given constraints. In this paper we describe
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Stephen Shemlon, Stephen Shemlon, Stanley M. Dunn, Stanley M. Dunn, } "Rule-based image interpretation with models of expected structure", Proc. SPIE 1233, Medical Imaging IV: Image Processing, (1 July 1990); doi: 10.1117/12.18886; https://doi.org/10.1117/12.18886

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