Vessels classification
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...
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2006
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my-uthm-ep.70862022-05-30T00:44:42Z Vessels classification 2006-04 Suriani, Nor Surayahani TA Engineering (General). Civil engineering (General) TA1501-1820 Applied optics. Photonics 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. 2006-04 Thesis http://eprints.uthm.edu.my/7086/ http://eprints.uthm.edu.my/7086/1/24p%20NOR%20SURAYAHANI%20SURIANI.pdf text en public mphil masters Universiti Teknologi Malaysia Fakulti Kejuruteraan Elektrik |
| institution |
Universiti Tun Hussein Onn Malaysia |
| collection |
UTHM Institutional Repository |
| language |
English |
| topic |
TA Engineering (General) Civil engineering (General) TA Engineering (General) Civil engineering (General) |
| spellingShingle |
TA Engineering (General) Civil engineering (General) TA Engineering (General) Civil engineering (General) Suriani, Nor Surayahani Vessels classification |
| description |
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. |
| format |
Thesis |
| qualification_name |
Master of Philosophy (M.Phil.) |
| qualification_level |
Master's degree |
| author |
Suriani, Nor Surayahani |
| author_facet |
Suriani, Nor Surayahani |
| author_sort |
Suriani, Nor Surayahani |
| title |
Vessels classification |
| title_short |
Vessels classification |
| title_full |
Vessels classification |
| title_fullStr |
Vessels classification |
| title_full_unstemmed |
Vessels classification |
| title_sort |
vessels classification |
| granting_institution |
Universiti Teknologi Malaysia |
| granting_department |
Fakulti Kejuruteraan Elektrik |
| publishDate |
2006 |
| url |
http://eprints.uthm.edu.my/7086/1/24p%20NOR%20SURAYAHANI%20SURIANI.pdf |
| _version_ |
1747831109506301952 |