Suriani, Nor Surayahani (2006) Vessels classification. Masters thesis, Universiti Teknologi Malaysia.
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Text (24 pages)
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Abstract
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 modelbased 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) |
|---|---|
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics |
| Divisions: | Faculty of Electrical and Electronic Engineering > Department of Electronic Enngineering |
| Depositing User: | Mrs. Nur Nadia Md. Jurimi |
| Date Deposited: | 02 Nov 2021 01:59 |
| Last Modified: | 02 Nov 2021 01:59 |
| URI: | http://eprints.uthm.edu.my/id/eprint/3165 |
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