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Proceedings Paper

Segmentation-Based Boundary Modeling For Natural Terrain Scenes
Author(s): Charles A. McNary; Diane K. Conti; Wilfried O. Eckhardt
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Paper Abstract

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.

Paper Details

Date Published: 21 February 1980
PDF: 9 pages
Proc. SPIE 0205, Image Understanding Systems II, (21 February 1980); doi: 10.1117/12.958170
Show Author Affiliations
Charles A. McNary, Hughes Research Laboratories (United States)
Diane K. Conti, Hughes Research Laboratories (United States)
Wilfried O. Eckhardt, Hughes Research Laboratories (United States)


Published in SPIE Proceedings Vol. 0205:
Image Understanding Systems II
Carol Clark, Editor(s)

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