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Proceedings Paper

Multidimensional Clustering�An Application To Three-Dimensional (3D) Surface Extraction
Author(s): C. M. Bjorklund; W. G. Eppler; J. J. Pearson
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Paper Abstract

Significant development has been made in programs to analyze and display multiple vector-valued clusters of input, due in large part to the usefulness of multispectral data in remote sensing. This paper describes recent results in laser range identification of building surfaces to illustrate the general utility of such analysis/display programs. A 4-tuple of features was computed at each pixel of a range image by fitting a plane to every 5 X 5 pixel region in the image. The four plane parameters (i.e., azimuth, elevation, length of the normal vector, and residual fit error) constitute a vector image to be histogrammed and clustered. The multidimensional clusters have actual 3-D descriptions (angles in degrees, distance and residual in meters) which provide insight into the relationships within the 3-D scene, and clarifies their value in planar surface identification. Interesting cluster categories can be designed in the original image. This capability also permits visual verification of the sensitivity of the cluster choice to the results obtained.

Paper Details

Date Published: 8 March 1982
PDF: 6 pages
Proc. SPIE 0302, Infrared Technology for Target Detection and Classification, (8 March 1982); doi: 10.1117/12.932631
Show Author Affiliations
C. M. Bjorklund, Lockheed Palo Alto Research Laboratory (United States)
W. G. Eppler, Lockheed Palo Alto Research Laboratory (United States)
J. J. Pearson, Lockheed Palo Alto Research Laboratory (United States)


Published in SPIE Proceedings Vol. 0302:
Infrared Technology for Target Detection and Classification
Pat M. Narendra, Editor(s)

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