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Proceedings Paper

Knowledge-Based Segmentation Of Texture Images With Application To Seismic Data Interpretation
Author(s): Zhen Zhang; M. Simaan
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Paper Abstract

In this paper, we discuss two control schemes, based on (1) centrally controlkd region-growing and (2) iterative quadtree splitting, for incorporating knowledge-based processes into the- segmentation of texture images. An important feature of these two schemes is that knowledge about the nature of the images is directly involved in the partition process rather than being used afterwards to label the resulting segments of the partition. Prototype systems which we implemented for the automatic interpretation of seismic sections are described in detail. A specific application of these systems on a test section of real seismic data from the Gulf of Mexico is presented. Test runs on the data have shown that both schemes give a much improved segmentation result over the one obtained by a conventional approach.

Paper Details

Date Published: 29 March 1988
PDF: 10 pages
Proc. SPIE 0937, Applications of Artificial Intelligence VI, (29 March 1988); doi: 10.1117/12.946961
Show Author Affiliations
Zhen Zhang, Medical University of South Carolina (United States)
M. Simaan, University of Pittsburgh (United States)


Published in SPIE Proceedings Vol. 0937:
Applications of Artificial Intelligence VI
Mohan M. Trivedi, Editor(s)

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