Autonomous Ship Classification By Moment Invariants

Budimir Zvolanek

doi:10.1117/12.932837

7 December 1981 Autonomous Ship Classification By Moment Invariants

Budimir Zvolanek

Proceedings Volume 0292, Processing of Images and Data from Optical Sensors; (1981) https://doi.org/10.1117/12.932837
Event: 25th Annual Technical Symposium, 1981, San Diego, United States

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.

Citation Download Citation

Budimir Zvolanek "Autonomous Ship Classification By Moment Invariants", Proc. SPIE 0292, Processing of Images and Data from Optical Sensors, (7 December 1981); https://doi.org/10.1117/12.932837

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available