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Vessels classification

Suriani, Nor Surayani (2006) Vessels classification. Masters thesis, Universiti Teknologi Malaysia.

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Moment based invariants, in various forms, have been widely used over the years as features for recognition in many areas of image analysis. The proposed work will look at offline ship recognition using ships silhouette images which will include recognition of part of an object for situations in which only part of the object is visible. The model-based classification is design using Image Processing MATLAB Toolbox. The moment invariant techniques apply for features extraction to obtain moment signatures to do classification. The minimum mean distance classifier is used to classify the ships which works based on the minimum distance feature vector. This research study will address some other issue of classification and various conditions of images that might exist in real environment.

Item Type:Thesis (Masters)
Additional Information:Master of Engineering Electrical (Electronics and Telecommunication )) - Universiti Teknologi Malaysia, 2006;
Uncontrolled Keywords:ship recognition, vessels classification, pattern recognition, invariant techniques
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
H Social Sciences > HE Transportation and Communications
Divisions:Electrical Engineering
ID Code:5567
Deposited By: Ms Zalinda Shuratman
Deposited On:18 Jun 2008 07:06
Last Modified:09 Jul 2012 03:35

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