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Conditional Estimation of Vector Patterns in Remote Sensing and GIS

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We examine minimum sufficient data requirements and digital compression methods for processing vector polygonal data using X11R6 libraries. Algorithms are developed for the fast import, export, and compaction of binary data using standard graphical primitives. Algorithms are shown to be adaptable to both raster and vector image processing depending on grafport conditions and the level of vector modeling required for pattern identification. The concepts of minimum sufficient data, data classification, and feature extraction are reviewed to understand how vector architectures compare with other data translation and image integration methods. The Discussion focuses on the efficient conversion of raster images to vector equivalent models for use within Computer Aided Design CAD and Geographic Information Systems GIS. All algorithms build upon prior research efforts outlined within the cumulative ERO research program. For example, data import and data export algorithms build upon previous functions and procedures developed for the fast import and export of raster data using traditional image processing techniques. Algorithms include raster and vector fields conducive to point, line, arc, and polygon geometric models. The effort is directed toward device independent computer architectures as a means to support common UNIX platforms. All data structures employ Open Systems Foundation OSF CC language techniques for direct application across heterogeneous networks.

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  • Numerical Mathematics
  • Theoretical Mathematics

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