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21 February 1980 Segmentation-Based Boundary Modeling For Natural Terrain Scenes
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This paper describes a segmentation-based boundary-modeling processor for natural terrain scenes. Techniques of this type can achieve high-precision trajectory updating with image-based guidance systems.1 The boundary-modeling processor is based on region extraction and was developed as an alternative to edge-based boundary-modeling techniques. Region boundaries provide a high degree of boundary connectivity and eliminate competing edge and line structure resulting from texture gray-level gradients. Segmentation thresholds are derived with an adaptive-averaging preprocessor, which enhances the modal structure of the image gray-level histogram by replacing local-region gray-level distributions (texture) with their mean values. A contrast-edge map can be used to validate the selection of gray-level thresholds for region segmentation by locally correlating the region boundary points with the contrast-edge map of the scene. With this refinement, the segmentation-based boundary-model processor can combine the best characteristics of region segmentation and contrast-edge extraction: a high degree of region-boundary connectivity and high spatial fidelity of the extracted edge points. The derived boundaries form models of curvilinear scene-boundary features that may be accessed at several levels of approximation for fix-area acquisition and precision fix-point identification. Scene-boundary models and hierarchical line representations of the curvilinear features were generated using this segmentation-based boundary-modeling processor for a variety of natural terrain scenes. The resultant models demonstrate the effectiveness of this processor and its utility in scene pattern matching.
© (1980) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Charles A. McNary, Diane K. Conti, and Wilfried O. Eckhardt "Segmentation-Based Boundary Modeling For Natural Terrain Scenes", Proc. SPIE 0205, Image Understanding Systems II, (21 February 1980);

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