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Proceedings Paper

Clustering algorithms for a PC-based hardware implementation of the unsupervised classifier for the shuttle ice detection system
Author(s): Sandeep Jaggi
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Paper Abstract

This paper introduces a near-real time method of image processing in a PC-based environment. A segmentation technique based on unsupervised classification is implemented. A prototype for the detection of ice formation on the external tank (ET) of the Space Shuttle is being developed for NASA Science and Technology Laboratory by Lockheed Engineering and Sciences Company at Stennis Space Center, MS. The objective is to be able to do an online classification of the ET images into distinct regions denoting ice, frost, wet, or dry areas. The images are acquired with an infrared camera and digitized before being processed by a computer to yield a false color-coded pattern, with each color representing a region. A two-monitor PC-based setup is used for image processing. Various techniques for classification, both supervised and unsupervised, are being investigated for developing a methodology. This paper discusses the implementation of two adaptive algorithms for image segmentation. The K-means algorithm is compared to another algorithm based on adaptive estimation of region boundaries.

Paper Details

Date Published: 1 April 1991
PDF: 9 pages
Proc. SPIE 1451, Nonlinear Image Processing II, (1 April 1991); doi: 10.1117/12.44335
Show Author Affiliations
Sandeep Jaggi, Lockheed Engineering & Sciences Co. (United States)

Published in SPIE Proceedings Vol. 1451:
Nonlinear Image Processing II
Edward R. Dougherty; Gonzalo R. Arce; Charles G. Boncelet, Editor(s)

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