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Proceedings Paper

Tracing And Identification Of Features Of Varying Characteristics Using A Vectored Mask
Author(s): Richard L. Sanford; Thomas Novak
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Paper Abstract

Tracing maps or other line-drawings for feature identification to obtain concurrent and subsequent identification is complicated by the difficulty in vectoring the tracing process in an optimal manner. The digitized features may have nearly infinite variation in size and shape, and it is important to measure the original shape including width, curvature, length, and continuity; thus image enhancement is undesirable. Without processing, including line-thinning, many traditional tracing algorithms result in backtracking or random wandering rather than following feature trends. The problem is especially acute in cases where features (such as lines) intersect. The necessity to intelligently monitor feature tracing is the subject of the current study presented in this paper. This paper reports on software development for an IBM PS/2 Model 80, with a 80386 processor, to control a mobile mask that focuses on a limited portion of the feature being traced. During tracing, the mask surrounds a portion of the feature, and an investigation of attributes becomes manageable since the field of view is restricted by the mask. After feature information has been extracted from the current location, the mask is vectored to a new location (based on current trend information) that is optimal for continually following the feature. Feature identification and trace vectoring is performed by using programming language (Turbo Pascal 5.0) manipulation of Boolean functions to simulate knowledge base rules. Program code is much more efficient during the software development stage than coupling an inference engine to the tracing software. In future research, integration with an inference engine will permit efficient user-initiated strategy changes for analyzing increasingly complex features.

Paper Details

Date Published: 21 March 1989
PDF: 11 pages
Proc. SPIE 1095, Applications of Artificial Intelligence VII, (21 March 1989); doi: 10.1117/12.969254
Show Author Affiliations
Richard L. Sanford, The University of Alabama (United States)
Thomas Novak, The University of Alabama (United States)


Published in SPIE Proceedings Vol. 1095:
Applications of Artificial Intelligence VII
Mohan M. Trivedi, Editor(s)

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