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Proceedings Paper

Autonomous Ship Classification By Moment Invariants
Author(s): Budimir Zvolanek
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Paper Abstract

An algorithm to classify ships from images generated by an infrared (IR) imaging sensor is described. The algorithm is based on decision-theoretic classification of Moment Invariant Functions (MIFs). The MIFs are computed from two-dimensional gray-level images to form a feature vector uniquely describing the ship. The MIF feature vector is classified by a Distance-Weighted k-Nearest Neighbor (D-W k-NN) decision rule to identify the ship type. Significant advantage of the MIF feature extraction coupled with D-W k-NN classification is the invariance of the classification accuracies to ship/sensor orienta-tion - aspect, depression, roll angles and range. The accuracy observed from a set of simulated IR test images reveals a good potential of the classifier algorithm for ship screening.

Paper Details

Date Published: 7 December 1981
PDF: 8 pages
Proc. SPIE 0292, Processing of Images and Data from Optical Sensors, (7 December 1981); doi: 10.1117/12.932837
Show Author Affiliations
Budimir Zvolanek, McDonnell Douglas Astronautics Company (United States)


Published in SPIE Proceedings Vol. 0292:
Processing of Images and Data from Optical Sensors
William H. Carter, Editor(s)

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