By Topic

Automatic segmentation of seismic data via knowledge-based image processing techniques

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Simaan, M. ; Dept. of Electr. Eng., Pittsburgh Univ., PA, USA ; Zhen Zhang

Three knowledge-based texture image segmentation systems are described, and their performance on a real test image is analyzed. The image is a small piece of a seismic section and the objective is to segment it into zones of common signal texture. The first system is based on a run length statistics algorithm extended by a decision process which incorporates heuristic rules to influence the segmentation. The second and third systems are based on texture energy measurement algorithms augmented by two different knowledge-based classification processes. The knowledge-based process of the second system is controlled by a parallel region growing scheme, and that of the third system is controlled by an iterative quadtree-splitting scheme. The results show that the third system, texture-energy measurement augmented by a knowledge-based quadtree-splitting process, provides a better segmentation of the test image than the other two

Published in:

Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on

Date of Conference:

3-6 Apr 1990