Publication:
Robust Recognition of Ship Types from an Infrared Silhouette

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Authors
Alves, Jorge
Herman, Jessica
Rowe, Neil C.
Subjects
Advisors
Date of Issue
2004-06
Date
June 2004
Publisher
Monterey, California. Naval Postgraduate School
Language
Abstract
Accurate identification of unknown contacts crucial in military intelligence. Automated systems that quickly and accurately determine the identity of a contact could be a benefit in backing up electronic-signals identification methods. This work reports two experimental systems for ship classification from infrared FLIR images. In an edge-histogram approach, we used the histogram of the binned distribution of observed straight edge segments of the ship image. Some simple tests had a classification success rate of 80% on silhouettes. In a more comprehensive neural-network approach, we calculated scale-invariant moments of a silhouette and used them as input to a neural network. We trained the network on several thousand perspectives of a wire-frame model of the outline of each of five ship classes. We obtained 70% accuracy with detailed tested on real infrared images but performance was more robust than with the edge-histogram approach.
Type
Thesis
Conference Paper
Description
This paper appeared in the Command and Control Research and Technology Symposium, San Diego, CA, June 2004.
Series/Report No
Department
Computer Science (CS)
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Sponsors
supported by the Naval Postgraduate School
Funder
funds provided by the Chief of Naval Research
Format
Citation
Command and Control Research and Technology Symposium, San Diego, CA, June 2004.
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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