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Proceedings Paper

Intelligent Autocueing Of Tactical Targets
Author(s): B. Bhanu; A. S. Politopoulos; B. A. Parvin
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Paper Abstract

In this paper we present a set of algorithms used to automatically detect, segment and classify tactical targets in FLIR (Forward Looking InfraRed) images. These algorithms are implemented in an Intelligent Automatic Target Cueing (IATC) system. Target localization and segmentation is carried out using an intelligent preprocessing step followed by relaxation or a modified double gate filter followed by difference operators. The techniques make use of range, intensity and edge density information. A set of robust features of the segmented targets is computed. These features are normalized and decorrelated. Feature selection is done using the Bhattacharrya measure. Classification techniques include a set of linear, quadratic classifiers, clustering algorithms, and an efficient K-nearest neighbor algorithm. Facilities exist to use structural information, to use feedback to obtain more refined boundaries of the targets and to adapt the cuer to the required mission. The IATC incorporating the above algorithms runs in an automatic mode. The results are shown on a FLIR data base consisting of 480, 512x512, 8 bit air-to-ground images.

Paper Details

Date Published: 9 January 1984
PDF: 8 pages
Proc. SPIE 0435, Architectures and Algorithms for Digital Image Processing, (9 January 1984); doi: 10.1117/12.936978
Show Author Affiliations
B. Bhanu, Ford Aerospace & Communications Corporation (United States)
A. S. Politopoulos, Ford Aerospace & Communications Corporation (United States)
B. A. Parvin, Ford Aerospace & Communications Corporation (United States)


Published in SPIE Proceedings Vol. 0435:
Architectures and Algorithms for Digital Image Processing
Per-Erik Danielsson; Andre J. Oosterlinck, Editor(s)

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