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Proceedings Paper

Segmentation And Classification Of Targets In Fur Imagery
Author(s): O. Robert Mitchell; Stephen M. Lutton
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Paper Abstract

An approach is described for detecting and classifying tactical targets in FLIR imagery. The basic assumption used for segmenting objects from their background is that the objects to be detected differ from the background in grey level, edge, properties, or texture. Potential targets are selected from a large frame, by locating combinations of grey level, edge, value, and texture that occur infrequently over the entire frame. Once potential objects are obtained, they are segmented from their backgrounds using the identical process as above, except applied on a local level. The segmented objects are classified into three, types of vehicles or into false, alarms. The classification procedure uses features measured on projections made through the segmented objects. Results are shown for 32 test images.

Paper Details

Date Published: 9 January 1979
PDF: 8 pages
Proc. SPIE 0155, Image Understanding Systems and Industrial Applications I, (9 January 1979); doi: 10.1117/12.956734
Show Author Affiliations
O. Robert Mitchell, Purdue University (United States)
Stephen M. Lutton, Purdue University (United States)


Published in SPIE Proceedings Vol. 0155:
Image Understanding Systems and Industrial Applications I
Ram Nevatia, Editor(s)

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