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Proceedings Paper

Radial signatures and their application to target recognition
Author(s): Hakan Bakircioglu; Erol Gelenbe
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Paper Abstract

In pattern recognition, it is crucial to be able to represent objects with feature that contain as much of the information as possible in compact form. A typical 8-bit grayscale digitized image can be sorted using M by N values that represent the intensity levels of individual pixels where M and N are image dimensions. Pattern recognition algorithms use various methods for feature extraction, like chain codes, Fourier descriptors, and invariant moments. We will propose features that will characterize objects much more efficiently. Our feature scan be viewed as basis functions that lead to a set of images within an equivalence class. In order to illustrate the method with an application, these features are then used to train a set of learning RNNs which can be used to detect targets within clutter with high accuracy, and to classify the targets or man-made objects from natural clutter. Experimental data from SAR imagery is used to illustrate the performance of the proposed algorithm. Currently, we are investigating the applicability of this approach to a set of GPR mine data.

Paper Details

Date Published: 2 August 1999
PDF: 10 pages
Proc. SPIE 3710, Detection and Remediation Technologies for Mines and Minelike Targets IV, (2 August 1999); doi: 10.1117/12.357119
Show Author Affiliations
Hakan Bakircioglu, Duke Univ. (United States)
Erol Gelenbe, Duke Univ. (United States)

Published in SPIE Proceedings Vol. 3710:
Detection and Remediation Technologies for Mines and Minelike Targets IV
Abinash C. Dubey; James F. Harvey; J. Thomas Broach; Regina E. Dugan, Editor(s)

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