NDLI: Unsupervised Learning Neural Network for Classification of Ship-Hull Fouling Conditions

Content Provider	IEEE Xplore Digital Library
Author	Pei-Fang Wang Lieberman, S. Liyen Ho
Copyright Year	2006
Description	Author affiliation: SSC San Diego, San Diego (Pei-Fang Wang)
Abstract	We have developed algorithms to automate image classification and quantification of fouling conditions for ship hulls. Motivated by the fact that existing methods of quantifying fouling conditions and physical oceanographic conditions (e.g., coral reef population) have to rely on manual and visual labors by "experts", which are highly subjective and time consuming, we have developed algorithms to classify and quantify fouling conditions of ship hulls from images taken underwater. The algorithms include three parts: image production, feature extraction, and neural network classifiers. Raw images reflecting various fouling conditions were produced. Sub-sampling was conducted on the raw images and a total of 360 sub-image samples were generated. The 360 sub-image samples were divided into two datasets, one with 300 samples for training and the other 60 samples for testing of a self-organizing map (SOM) neural network. For each image, a total of 32 feature variables extracted from the statistical method of SGLDM were used for training the SOM neural network. Results show that with adequate training, the success rate for prediction can reach 100%.
Starting Page	4601
Ending Page	4604
File Size	625202
Page Count	4
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.247089
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Unsupervised learning Neural networks Marine vehicles Feature extraction Image processing Industrial training Friction Unmanned aerial vehicles Flowcharts Image sampling
Content Type	Text
Resource Type	Article

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