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 whi...
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2006
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/5567/1/NorSurayahaniSurianiMFKE2006.pdf |
| _version_ | 1825909761344798720 |
|---|---|
| author | Suriani, Nor Surayani |
| author_facet | Suriani, Nor Surayani |
| author_sort | Suriani, Nor Surayani |
| collection | ePrints |
| 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 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. |
| first_indexed | 2024-03-05T18:06:58Z |
| format | Thesis |
| id | utm.eprints-5567 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T18:06:58Z |
| publishDate | 2006 |
| record_format | dspace |
| spelling | utm.eprints-55672018-09-17T03:03:09Z http://eprints.utm.my/5567/ Vessels classification Suriani, Nor Surayani TK Electrical engineering. Electronics Nuclear engineering HE Transportation and Communications 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. 2006-04 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/5567/1/NorSurayahaniSurianiMFKE2006.pdf Suriani, Nor Surayani (2006) Vessels classification. Masters thesis, Universiti Teknologi Malaysia. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:61973 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering HE Transportation and Communications Suriani, Nor Surayani Vessels classification |
| title | Vessels classification |
| title_full | Vessels classification |
| title_fullStr | Vessels classification |
| title_full_unstemmed | Vessels classification |
| title_short | Vessels classification |
| title_sort | vessels classification |
| topic | TK Electrical engineering. Electronics Nuclear engineering HE Transportation and Communications |
| url | http://eprints.utm.my/5567/1/NorSurayahaniSurianiMFKE2006.pdf |
| work_keys_str_mv | AT surianinorsurayani vesselsclassification |