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Proceedings Paper

Autonomous ship classification using synthetic and real color images
Author(s): Deniz Kumlu; B. Keith Jenkins
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Paper Abstract

This work classifies color images of ships attained using cameras mounted on ships and in harbors. Our data-sets contain 9 different types of ship with 18 different perspectives for our training set, development set and testing set. The training data-set contains modeled synthetic images; development and testing data-sets contain real images. The database of real images was gathered from the internet, and 3D models for synthetic images were imported from Google 3D Warehouse. A key goal in this work is to use synthetic images to increase overall classification accuracy. We present a novel approach for autonomous segmentation and feature extraction for this problem. Support vector machine is used for multi-class classification. This work reports three experimental results for multi-class ship classification problem. First experiment trains on a synthetic image data-set and tests on a real image data-set, and obtained accuracy is 87.8%. Second experiment trains on a real image data-set and tests on a separate real image data-set, and obtained accuracy is 87.8%. Last experiment trains on real + synthetic image data-sets (combined data-set) and tests on a separate real image data-set, and obtained accuracy is 93.3%.

Paper Details

Date Published: 6 March 2013
PDF: 11 pages
Proc. SPIE 8661, Image Processing: Machine Vision Applications VI, 86610M (6 March 2013); doi: 10.1117/12.2005749
Show Author Affiliations
Deniz Kumlu, The Univ. of Southern California (United States)
B. Keith Jenkins, The Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 8661:
Image Processing: Machine Vision Applications VI
Philip R. Bingham; Edmund Y. Lam, Editor(s)

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