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Proceedings Paper

Knowledge-Based Image Segmentation
Author(s): T. Kasparis; N. M. Marinovic; G. Eichmann
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Paper Abstract

Image segmentation is a highly scene dependent and problem dependent decision making or pattern recognition process. Knowledge about the class of imagess to be processed and the tasks to be performed, plays an important role. Two approaches that explicitly incorporate such knowledge are advanced for the class of images containing polygonal shapes. They can be generalized to other shapes by change of pre-processing steps. Inference is both data driven and goal driven. It is guided by meta rules that are fired by the outputs of preprocessing. Effective suppression of noise is achieved. The methods illustrate the potential of AI techniques and tools for low-level image understanding tasks.

Paper Details

Date Published: 27 March 1987
PDF: 6 pages
Proc. SPIE 0726, Intelligent Robots and Computer Vision V, (27 March 1987); doi: 10.1117/12.937741
Show Author Affiliations
T. Kasparis, The City College of the City University of New York (United States)
N. M. Marinovic, The City College of the City University of New York (United States)
G. Eichmann, The City College of the City University of New York (United States)


Published in SPIE Proceedings Vol. 0726:
Intelligent Robots and Computer Vision V
David P. Casasent, Editor(s)

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