Enhanced Clustering Algorithms For Gray-Scale Image Segmentation
The clustering algorithms are widely used as an unsupervised method for image segmentation in medical diagnosis, satellite imaging and biometric systems. The algorithms are chosen since they are easy to be implemented, required low computational time and less sensitive to noise and artifacts. Howeve...
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| Format: | Thesis |
| Language: | English |
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2012
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| Online Access: | http://eprints.usm.my/41804/1/FASAHAT_ULLAH_SIDDIQUI.pdf |
| _version_ | 1825834498690908160 |
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| author | Siddiqui, Fasahat Ullah |
| author_facet | Siddiqui, Fasahat Ullah |
| author_sort | Siddiqui, Fasahat Ullah |
| collection | USM |
| description | The clustering algorithms are widely used as an unsupervised method for image segmentation in medical diagnosis, satellite imaging and biometric systems. The algorithms are chosen since they are easy to be implemented, required low computational time and less sensitive to noise and artifacts. However, in some cases the conventional clustering algorithms introduce over-segmentation problems and unable to preserve the region of interest (i.e. objects). |
| first_indexed | 2024-03-06T15:23:39Z |
| format | Thesis |
| id | usm.eprints-41804 |
| institution | Universiti Sains Malaysia |
| language | English |
| last_indexed | 2024-03-06T15:23:39Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | usm.eprints-418042019-04-12T05:26:22Z http://eprints.usm.my/41804/ Enhanced Clustering Algorithms For Gray-Scale Image Segmentation Siddiqui, Fasahat Ullah TK1-9971 Electrical engineering. Electronics. Nuclear engineering The clustering algorithms are widely used as an unsupervised method for image segmentation in medical diagnosis, satellite imaging and biometric systems. The algorithms are chosen since they are easy to be implemented, required low computational time and less sensitive to noise and artifacts. However, in some cases the conventional clustering algorithms introduce over-segmentation problems and unable to preserve the region of interest (i.e. objects). 2012-04 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/41804/1/FASAHAT_ULLAH_SIDDIQUI.pdf Siddiqui, Fasahat Ullah (2012) Enhanced Clustering Algorithms For Gray-Scale Image Segmentation. Masters thesis, Universiti Sains Malaysia. |
| spellingShingle | TK1-9971 Electrical engineering. Electronics. Nuclear engineering Siddiqui, Fasahat Ullah Enhanced Clustering Algorithms For Gray-Scale Image Segmentation |
| title | Enhanced Clustering Algorithms For Gray-Scale Image Segmentation |
| title_full | Enhanced Clustering Algorithms For Gray-Scale Image Segmentation |
| title_fullStr | Enhanced Clustering Algorithms For Gray-Scale Image Segmentation |
| title_full_unstemmed | Enhanced Clustering Algorithms For Gray-Scale Image Segmentation |
| title_short | Enhanced Clustering Algorithms For Gray-Scale Image Segmentation |
| title_sort | enhanced clustering algorithms for gray scale image segmentation |
| topic | TK1-9971 Electrical engineering. Electronics. Nuclear engineering |
| url | http://eprints.usm.my/41804/1/FASAHAT_ULLAH_SIDDIQUI.pdf |
| work_keys_str_mv | AT siddiquifasahatullah enhancedclusteringalgorithmsforgrayscaleimagesegmentation |