Segmentation using models of expected structure
Stephen Shemlon, Tajen Liang, Kyugon Cho, STANLEY M. DUNN · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1991
This paper outlines the framework of an image segmentation system based on the expected presenialion of objects in an image. The paradigm uses models that best characterize those objects that are likely to be present in a scene as captured by a given image formation process. We present the parameters for describing the expected presentations and show how they can be developed into a regionbased image algebra that is a generalized mechanism for reasoning and planning image segmentation and subsequent machine learning tasks. We present results of experiments with Transmission Electron Microscope (TEM) serial sections aerial photographs of urban scenes Mill brain scans and dental radiographs.