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Proceedings Paper

Recognition Of Complex Graphical Objects
Author(s): P. Puliti; G. Tascini
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Paper Abstract

The aim of the paper is twofold: firstly defining a method for object extraction from gray-level graphical images; secondly recognizing graphical objects in order to return an automatic description of a given complex drawing. The segmentation uses an histogram mode clustering, which groups the pixels by gray-level intensity in order to define a series of thresholds. A multithreshold method is developed to insert local properties in the multimodal hystogram and to realize an automatic threshold selection. The automatic detection of gray-level images, representing technical drawing, may be simplified by some given drawing criteria. In order to recognize the graphical objects a gool-driven approach is adopted. A structural model is then defined in which the domain knowledge is represented by a semantic network. Finally the semantic network knowledge is used to recognize part or set of parts of a technical drawing, according to a given strategy.

Paper Details

Date Published: 2 March 1989
PDF: 7 pages
Proc. SPIE 1027, Image Processing II, (2 March 1989); doi: 10.1117/12.950279
Show Author Affiliations
P. Puliti, University of Ancona (Italy)
G. Tascini, University of Ancona (Italy)

Published in SPIE Proceedings Vol. 1027:
Image Processing II
Peter J.S. Hutzler; Andre J. Oosterlinck, Editor(s)

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